CySecurity News - Latest Information Security and Hacking Incidents: Artificial Intelligence

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Can AI Be Trusted With Sensitive Business Data?

As artificial intelligence becomes more common in businesses, from retail to finance to technology— it’s helping teams make faster decisions. But behind these smart predictions is a growing problem: how do you make sure employees only see what they’re allowed to, especially when AI mixes information from many different places?

Take this example: A retail company’s AI tool predicts upcoming sales trends. To do this, it uses both public market data and private customer records. The output looks clean and useful but what if that forecast is shown to someone who isn’t supposed to access sensitive customer details? That’s where access control becomes tricky.

Why Traditional Access Rules Don’t Work for AI

In older systems, access control was straightforward. Each person had certain permissions: developers accessed code, managers viewed reports, and so on. But AI changes the game. These systems pull data from multiple sources, internal files, external APIs, sensor feeds, and combine everything to create insights. That means even if a person only has permission for public data, they might end up seeing results that are based, in part, on private or restricted information.

Why It Matters

Security Concerns: If sensitive data ends up in the wrong hands even indirectly, it can lead to data leaks. A 2025 study showed that over two-thirds of companies had AI-related security issues due to weak access controls.

Legal Risks: Privacy laws like the GDPR require clear separation of data. If a prediction includes restricted inputs and is shown to the wrong person, companies can face heavy fines.

Trust Issues: When employees or clients feel their data isn’t safe, they lose trust in the system, and the business.

What’s Making This So Difficult?

1. AI systems often blend data so deeply that it’s hard to tell what came from where.

2. Access rules are usually fixed, but AI relies on fast-changing data.

3. Companies have many users with different roles and permissions, making enforcement complicated.

4. Permissions are often too broad, for example, someone allowed to "view reports" might accidentally access sensitive content.

How Can Businesses Fix This?

• Track Data Origins: Label data as "public" or "restricted" and monitor where it ends up.

• Flexible Access Rules: Adjust permissions based on user roles and context.

• Filter Outputs: Build AI to hide or mask parts of its response that come from private sources.

• Separate Models: Train different AI models for different user groups, each with its own safe data.

• Monitor Usage: Keep logs of who accessed what, and use alerts to catch suspicious activity.

As AI tools grow more advanced and rely on live data from many sources, managing access will only get harder. Businesses must modernize their security strategies to protect sensitive information without slowing down innovation.

Is Your Bank Login at Risk? How Chatbots May Be Guiding Users to Phishing Scams

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Scammers

Artificial Intelligence Bank Details financial risk LLMs Mobile Security phishing Scammers

Is Your Bank Login at Risk? How Chatbots May Be Guiding Users to Phishing Scams

Cybersecurity researchers have uncovered a troubling risk tied to how popular AI chatbots answer basic questions. When asked where to log in to well-known websites, some of these tools may unintentionally direct users to the wrong places, putting their private information at risk.

Phishing is one of the oldest and most dangerous tricks in the cybercrime world. It usually involves fake websites that look almost identical to real ones. People often get an email or message that appears to be from a trusted company, like a bank or online store. These messages contain links that lead to scam pages. If you enter your username and password on one of these fake sites, the scammer gets full access to your account.

Now, a team from the cybersecurity company Netcraft has found that even large language models or LLMs, like the ones behind some popular AI chatbots, may be helping scammers without meaning to. In their study, they tested how accurately an AI chatbot could provide login links for 50 well-known companies across industries such as finance, retail, technology, and utilities.

The results were surprising. The chatbot gave the correct web address only 66% of the time. In about 29% of cases, the links led to inactive or suspended pages. In 5% of cases, they sent users to a completely different website that had nothing to do with the original question.

So how does this help scammers? Cybercriminals can purchase these unclaimed or inactive domain names, the incorrect ones suggested by the AI, and turn them into realistic phishing pages. If people click on them, thinking they’re going to the right site, they may unknowingly hand over sensitive information like their bank login or credit card details.

In one example observed by Netcraft, an AI-powered search tool redirected users who asked about a U.S. bank login to a fake copy of the bank’s website. The real link was shown further down the results, increasing the risk of someone clicking on the wrong one.

Experts also noted that smaller companies, such as regional banks and mid-sized fintech platforms, were more likely to be affected than global giants like Apple or Google. These smaller businesses may not have the same resources to secure their digital presence or respond quickly when problems arise.

The researchers explained that this problem doesn't mean the AI tools are malicious. However, these models generate answers based on patterns, not verified sources and that can lead to outdated or incorrect responses.

The report serves as a strong reminder: AI is powerful, but it is not perfect. Until improvements are made, users should avoid relying on AI-generated links for sensitive tasks. When in doubt, type the website address directly into your browser or use a trusted bookmark.

Amid Federal Crackdown, Microsoft Warns Against Rising North Korean Jobs Scams

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North Korea

AI Artificial Intelligence Cloud Cyber Crime Data Deepfakes Extortion Internet lazarus North Korea

Amid Federal Crackdown, Microsoft Warns Against Rising North Korean Jobs Scams

North Korean hackers are infiltrating high-profile US-based tech firms through scams. Recently, they have even advanced their tactics, according to the experts. In a recent investigation by Microsoft, the company has requested its peers to enforce stronger pre-employment verification measures and make policies to stop unauthorized IT management tools.

Further investigation by the US government revealed that these actors were working to steal money for the North Korean government and use the funds to run its government operations and its weapons program.

US imposes sanctions against North Korea

The US has imposed strict sanctions on North Korea, which restrict US companies from hiring North Korean nationals. It has led to threat actors making fake identities and using all kinds of tricks (such as VPNs) to obscure their real identities and locations. This is being done to avoid getting caught and get easily hired.

Recently, the threat actors have started using spoof tactics such as voice-changing tools and AI-generated documents to appear credible. In one incident, the scammers somehow used an individual residing in New Jersey, who set up shell companies to fool victims into believing they were paying a legitimate local business. The same individual also helped overseas partners to get recruited.

DoJ arrests accused

The clever campaign has now come to an end, as the US Department of Justice (DoJ) arrested and charged a US national called Zhenxing “Danny” Wanf with operating a “year-long” scam. The scheme earned over $5 million. The agency also arrested eight more people - six Chinese and two Taiwanese nationals. The arrested individuals are charged with money laundering, identity theft, hacking, sanctions violations, and conspiring to commit wire fraud.

In addition to getting paid in these jobs, which Microsoft says is a hefty payment, these individuals also get access to private organization data. They exploit this access by stealing sensitive information and blackmailing the company.

Lazarus group behind such scams

One of the largest and most infamous hacking gangs worldwide is the North Korean state-sponsored group, Lazarus. According to experts, the gang extorted billions of dollars from the Korean government through similar scams. The entire campaign is popular as “Operation DreamJob”.

"To disrupt this activity and protect our customers, we’ve suspended 3,000 known Microsoft consumer accounts (Outlook/Hotmail) created by North Korean IT workers," said Microsoft.

How Ransomware Has Impacted Cyber Insurance Assessment Approach

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Ransomware

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How Ransomware Has Impacted Cyber Insurance Assessment Approach

Cyber insurance and ransomware

The surge in ransomware campaigns has compelled cyber insurers to rethink their security measures. Ransomware attacks have been a threat for many years, but it was only recently that threat actors realized the significant financial benefits they could reap from such attacks. The rise of ransomware-as-a-service (RaaS) and double extortion tactics has changed the threat landscape, as organizations continue to fall victim and suffer data leaks that are accessible to everyone.

According to a 2024 threat report by Cisco, "Ransomware remains a prevalent threat as it directly monetizes attacks by holding data or systems hostage for ransom. Its high profitability, coupled with the increasing availability of ransomware-as-a-service platforms, allows even less skilled attackers to launch campaigns."

Changing insurance landscape due to ransomware

Cyber insurance is helping businesses to address such threats by offering services such as ransom negotiation, ransom reimbursement, and incident response. Such support, however, comes with a price. The years 2020 and 2021 witnessed a surge in insurance premiums. The Black Hat USA conference, scheduled in Las Vegas, will discuss how ransomware has changed businesses’ partnerships with insurers. Ransomware impacts an organization’s business model.

At the start of the 21st century, insurance firms required companies to buy a security audit to get a 25% policy discount. Insurance back then used to be a hands-on approach. The 2000s were followed by the data breach era; however, breaches were less common and frequent, targeting the hospitality and retail sectors.

This caused insurers to stop checking for in-depth security audits, and they began using questionnaires to measure risk. In 2019, the ransomware wave happened, and insurers started paying out more claims than they were accepting. It was a sign that the business model was inadequate.

Questionnaires tend to be tricky for businesses to fill out. For instance, multifactor authentication (MFA) can be a complicated question to answer. Besides questionnaires, insurers have started using scans.

Incentives to promote security measures

Threats have risen, but so have assessments, coverage incentives like vanishing retention mean that if policy users follow security instructions, retention disappears. Safety awareness training and patching vulnerabilities are other measures that can help in cost reductions. Scanning assessment can help in premium pricing, as it is lower currently.

Cybercriminals Target AI Enthusiasts with Fake Websites to Spread Malware

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Websites

Artificial Intelligence Fake AI SEO URL Websites

Cybercriminals Target AI Enthusiasts with Fake Websites to Spread Malware

Cyber attackers are now using people’s growing interest in artificial intelligence (AI) to distribute harmful software. A recent investigation has uncovered that cybercriminals are building fake websites designed to appear at the top of Google search results for popular AI tools. These deceptive sites are part of a strategy known as SEO poisoning, where attackers manipulate search engine algorithms to increase the visibility of malicious web pages.

Once users click on these links believing they’re accessing legitimate AI platforms they’re silently redirected to dangerous websites where malware is secretly downloaded onto their systems. The websites use layers of code and redirection to hide the true intent from users and security software.

According to researchers, the malware being delivered includes infostealers— a type of software that quietly gathers personal and system data from a user’s device. These can include saved passwords, browser activity, system information, and more. One type of malware even installs browser extensions designed to steal cryptocurrency.

What makes these attacks harder to detect is the attackers' use of trusted platforms. For example, the malicious code is sometimes hosted on widely used cloud services, making it seem like normal website content. This helps the attackers avoid detection by antivirus tools and security analysts.

The way these attacks work is fairly complex. When someone visits one of the fake AI websites, their browser is immediately triggered to run hidden JavaScript. This script gathers information about the visitor’s browser, encrypts it, and sends it to a server controlled by the attacker. Based on this information, the server then redirects the user to a second website. That second site checks details like the visitor’s IP address and location to decide whether to proceed with delivering the final malicious file.

This final step often results in the download of harmful software that invades the victim’s system and begins stealing data or installing other malicious tools.

These attacks are part of a growing trend where the popularity of new technologies, such as AI chatbots is being exploited by cybercriminals for fraudulent purposes. Similar tactics have been observed in the past, including misleading users with fake tools and promoting harmful applications through hijacked social media pages.

As AI tools become more common, users should remain alert while searching for or downloading anything related to them. Even websites that appear high in search engine results can be dangerous if not verified properly.

To stay safe, avoid clicking on unfamiliar links, especially when looking for AI services. Always download tools from official sources, and double-check website URLs. Staying cautious and informed is one of the best ways to avoid falling victim to these evolving online threats.

OpenAI Rolls Out Premium Data Connections for ChatGPT Users

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Artificial Intelligence ChatGPT Connectors CyberThreat Data Connections Drive Dropbox Emails Gmail Google GPT-4 OpenAI Privacy Twitter X

OpenAI Rolls Out Premium Data Connections for ChatGPT Users

The ChatGPT solution has become a transformative artificial intelligence solution widely adopted by individuals and businesses alike seeking to improve their operations. Developed by OpenAI, this sophisticated artificial intelligence platform has been proven to be very effective in assisting users with drafting compelling emails, developing creative content, or conducting complex data analysis by streamlining a wide range of workflows.

OpenAI is continuously enhancing ChatGPT's capabilities through new integrations and advanced features that make it easier to integrate into the daily workflows of an organisation; however, an understanding of the platform's pricing models is vital for any organisation that aims to use it efficiently on a day-to-day basis. A business or an entrepreneur in the United Kingdom that is considering ChatGPT's subscription options may find that managing international payments can be an additional challenge, especially when the exchange rate fluctuates or conversion fees are hidden.

In this context, the Wise Business multi-currency credit card offers a practical solution for maintaining financial control as well as maintaining cost transparency. This payment tool, which provides companies with the ability to hold and spend in more than 40 currencies, enables them to settle subscription payments without incurring excessive currency conversion charges, which makes it easier for them to manage budgets as well as adopt cutting-edge technology.

A suite of premium features has been recently introduced by OpenAI that aims to enhance the ChatGPT experience for subscribers by enhancing its premium features. There is now an option available to paid users to use advanced reasoning models that include O1 and O3, which allow users to make more sophisticated analytical and problem-solving decisions.

The subscription comes with more than just enhanced reasoning; it also includes an upgraded voice mode that makes conversational interactions more natural, as well as improved memory capabilities that allow the AI to retain context over the course of a long period of time. It has also been enhanced with the addition of a powerful coding assistant designed to help developers automate workflows and speed up the software development process.

To expand the creative possibilities even further, OpenAI has adjusted token limits, which allow for greater amounts of input and output text and allow users to generate more images without interruption. In addition to expedited image generation via a priority queue, subscribers have the option of achieving faster turnaround times during high-demand periods.

In addition to maintaining full access to the latest models, paid accounts are also provided with consistent performance, as they are not forced to switch to less advanced models when server capacity gets strained-a limitation that free users may still have to deal with. While OpenAI has put in a lot of effort into enriching the paid version of the platform, the free users have not been left out. GPT-4o has effectively replaced the older GPT-4 model, allowing complimentary accounts to take advantage of more capable technology without having to fall back to a fallback downgrade.

In addition to basic imaging tools, free users will also receive the same priority in generation queues as paid users, although they will also have access to basic imaging tools. With its dedication to making AI broadly accessible, OpenAI has made additional features such as ChatGPT Search, integrated shopping assistance, and limited memory available free of charge, reflecting its commitment to making AI accessible to the public.

ChatGPT's free version continues to be a compelling option for people who utilise the software only sporadically-perhaps to write occasional emails, research occasionally, and create simple images. In addition, individuals or organisations who frequently run into usage limits, such as waiting for long periods of time for token resettings, may find that upgrading to a paid plan is an extremely beneficial decision, as it unlocks uninterrupted access as well as advanced capabilities.

In order to transform ChatGPT into a more versatile and deeply integrated virtual assistant, OpenAI has introduced a new feature, called Connectors, which is designed to transform the platform into an even more seamless virtual assistant. It has been enabled by this new feature for ChatGPT to seamlessly interface with a variety of external applications and data sources, allowing the AI to retrieve and synthesise information from external sources in real time while responding to user queries.

With the introduction of Connectors, the company is moving forward towards providing a more personal and contextually relevant experience for our users. In the case of an upcoming family vacation, for example, ChatGPT can be instructed by users to scan their Gmail accounts in order to compile all correspondence regarding the trip. This allows users to streamline travel plans rather than having to go through emails manually.

With its level of integration, Gemini is similar to its rivals, which enjoy advantages from Google's ownership of a variety of popular services such as Gmail and Calendar. As a result of Connectors, individuals and businesses will be able to redefine how they engage with AI tools in a new way. OpenAI intends to create a comprehensive digital assistant by giving ChatGPT secure access to personal or organisational data that is residing across multiple services, by creating an integrated digital assistant that anticipates needs, surfaces critical insights, streamlines decision-making processes, and provides insights.

There is an increased demand for highly customised and intelligent assistance, which is why other AI developers are likely to pursue similar integrations to remain competitive. The strategy behind Connectors is ultimately to position ChatGPT as a central hub for productivity — an artificial intelligence that is capable of understanding, organising, and acting upon every aspect of a user’s digital life.

In spite of the convenience and efficiency associated with this approach, it also illustrates the need to ensure that personal information remains protected while providing robust data security and transparency in order for users to take advantage of these powerful integrations as they become mainstream. In its official X (formerly Twitter) account, OpenAI has recently announced the availability of Connectors that can integrate with Google Drive, Dropbox, SharePoint, and Box as part of ChatGPT outside of the Deep Research environment.

As part of this expansion, users will be able to link their cloud storage accounts directly to ChatGPT, enabling the AI to retrieve and process their personal and professional data, enabling it to create responses on their own. As stated by OpenAI in their announcement, this functionality is "perfect for adding your own context to your ChatGPT during your daily work," highlighting the company's ambition of making ChatGPT more intelligent and contextually aware.

It is important to note, however, that access to these newly released Connectors is confined to specific subscriptions and geographical restrictions. A ChatGPT Pro subscription, which costs $200 per month, is exclusive to ChatGPT Pro subscribers only and is currently available worldwide, except for the European Economic Area (EEA), Switzerland and the United Kingdom. Consequently, users whose plans are lower-tier, such as ChatGPT Plus subscribers paying $20 per month, or who live in Europe, cannot use these integrations at this time.

Typically, the staggered rollout of new technologies is a reflection of broader challenges associated with regulatory compliance within the EU, where stricter data protection regulations as well as artificial intelligence governance frameworks often delay their availability. Deep Research remains relatively limited in terms of the Connectors available outside the company. However, Deep Research provides the same extensive integration support as Deep Research does.

In the ChatGPT Plus and Pro packages, users leveraging Deep Research capabilities can access a much broader array of integrations — for example, Outlook, Teams, Gmail, Google Drive, and Linear — but there are some restrictions on regions as well. Additionally, organisations with Team plans, Enterprise plans, or Educational plans have access to additional Deep Research features, including SharePoint, Dropbox, and Box, which are available to them as part of their Deep Research features.

Additionally, OpenAI is now offering the Model Context Protocol (MCP), a framework which allows workspace administrators to create customised Connectors based on their needs. By integrating ChatGPT with proprietary data systems, organizations can create secure, tailored integrations, enabling highly specialized use cases for internal workflows and knowledge management that are highly specialized.

With the increasing adoption of artificial intelligence solutions by companies, it is anticipated that the catalogue of Connectors will rapidly expand, offering users the option of incorporating external data sources into their conversations. The dynamic nature of this market underscores that technology giants like Google have the advantage over their competitors, as their AI assistants, such as Gemini, can be seamlessly integrated throughout all of their services, including the search engine.

The OpenAI strategy, on the other hand, relies heavily on building a network of third-party integrations to create a similar assistant experience for its users. It is now generally possible to access the new Connectors in the ChatGPT interface, although users will have to refresh their browsers or update the app in order to activate the new features.

As AI-powered productivity tools continue to become more widely adopted, the continued growth and refinement of these integrations will likely play a central role in defining the future of AI-powered productivity tools. A strategic approach is recommended for organisations and professionals evaluating ChatGPT as generative AI capabilities continue to mature, as it will help them weigh the advantages and drawbacks of deeper integration against operational needs, budget limitations, and regulatory considerations that will likely affect their decisions.

As a result of the introduction of Connectors and the advanced subscription tiers, people are clearly on a trajectory toward more personalised and dynamic AI assistance, which is able to ingest and contextualise diverse data sources. As a result of this evolution, it is also becoming increasingly important to establish strong frameworks for data governance, to establish clear controls for access to the data, and to ensure adherence to privacy regulations.

If companies intend to stay competitive in an increasingly automated landscape by investing early in these capabilities, they can be in a better position to utilise the potential of AI and set clear policies that balance innovation with accountability by leveraging the efficiencies of AI in the process. In the future, the organisations that are actively developing internal expertise, testing carefully selected integrations, and cultivating a culture of responsible AI usage will be the most prepared to fully realise the potential of artificial intelligence and to maintain a competitive edge for years to come.

Doctors Warned Over Use of Unapproved AI Tools to Record Patient Conversations

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Technology

Artificial Intelligence AVT Data protection england NHS Technology

Doctors Warned Over Use of Unapproved AI Tools to Record Patient Conversations

Healthcare professionals in the UK are under scrutiny for using artificial intelligence tools that haven’t been officially approved to record and transcribe conversations with patients. A recent investigation has uncovered that several doctors and medical facilities are relying on AI software that does not meet basic safety and data protection requirements, raising serious concerns about patient privacy and clinical safety.

This comes despite growing interest in using artificial intelligence to help doctors with routine tasks like note-taking. Known as Ambient Voice Technology (AVT), these tools are designed to save time by automatically recording and summarising patient consultations. In theory, this allows doctors to focus more on care and less on paperwork. However, not all AVT tools being used in medical settings have passed the necessary checks set by national authorities.

Earlier this year, NHS England encouraged the use of AVT and outlined the minimum standards required for such software. But in a more recent internal communication dated 9 June, the agency issued a clear warning. It stated that some AVT providers are not following NHS rules, yet their tools are still being adopted in real-world clinical settings.

The risks associated with these non-compliant tools include possible breaches of patient confidentiality, financial liabilities, and disruption to the wider digital strategy of the NHS. Some AI programs may also produce inaccurate outputs— a phenomenon known as “hallucination”— which can lead to serious errors in medical records or decision-making.

The situation has left many general practitioners in a difficult position. While eager to embrace new technologies, many lack the technical expertise to determine whether a product is safe and compliant. Dr. David Wrigley, a senior representative of the British Medical Association, stressed the need for stronger guidance and oversight. He believes doctors should not be left to evaluate software quality alone and that central NHS support is essential to prevent unsafe usage.

Healthcare leaders are also concerned about the growing number of lesser-known AI companies aggressively marketing their tools to individual clinics and hospitals. With many different options flooding the market, there’s a risk that unsafe or poorly regulated tools might slip through the cracks.

Matthew Taylor, head of the NHS Confederation, called the situation a “turning point” and suggested that national authorities need to offer clearer recommendations on which AI systems are safe to use. Without such leadership, he warned, the current approach could become chaotic and risky.

Interestingly, the UK Health Secretary recently acknowledged that some doctors are already experimenting with AVT tools before receiving official approval. While not endorsing this behaviour, he saw it as a sign that healthcare workers are open to digital innovation.

On a positive note, some AVT software does meet current NHS standards. One such tool, Accurx Scribe, is being used successfully and is developed in close consultation with NHS leaders.

As AI continues to reshape healthcare, experts agree on one thing: innovation must go hand-in-hand with accountability and safety.

U.S. Senators Propose New Task Force to Tackle AI-Based Financial Scams

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Technology

Artificial Intelligence Deepfake Scams Technology

U.S. Senators Propose New Task Force to Tackle AI-Based Financial Scams

In response to the rising threat of artificial intelligence being used for financial fraud, U.S. lawmakers have introduced a new bipartisan Senate bill aimed at curbing deepfake-related scams.

The bill, called the Preventing Deep Fake Scams Act, has been brought forward by Senators from both political parties. If passed, it would lead to the formation of a new task force headed by the U.S. Department of the Treasury. This group would bring together leaders from major financial oversight bodies to study how AI is being misused in scams, identity theft, and data-related crimes and what can be done about it.

The proposed task force would include representatives from agencies such as the Federal Reserve, the Consumer Financial Protection Bureau, and the Federal Deposit Insurance Corporation, among others. Their goal will be to closely examine the growing use of AI in fraudulent activities and provide the U.S. Congress with a detailed report within a year.

This report is expected to outline:

• How financial institutions can better use AI to stop fraud before it happens,

• Ways to protect consumers from being misled by deepfake content, and

• Policy and regulatory recommendations for addressing this evolving threat.

One of the key concerns the bill addresses is the use of AI to create fake voices and videos that mimic real people. These deepfakes are often used to deceive victims—such as by pretending to be a friend or family member in distress—into sending money or sharing sensitive information.

According to official data from the Federal Trade Commission, over $12.5 billion was stolen through fraud in the past year—a 25% increase from the previous year. Many of these scams now involve AI-generated messages and voices designed to appear highly convincing.

While this particular legislation focuses on financial scams, it adds to a broader legislative effort to regulate the misuse of deepfake technology. Earlier this year, the U.S. House passed a bill targeting nonconsensual deepfake pornography. Meanwhile, law enforcement agencies have warned that fake messages impersonating high-ranking officials are being used in various schemes targeting both current and former government personnel.

Another Senate bill, introduced recently, seeks to launch a national awareness program led by the Commerce Department. This initiative aims to educate the public on how to recognize AI-generated deception and avoid becoming victims of such scams.

As digital fraud evolves, lawmakers are urging financial institutions, regulators, and the public to work together in identifying threats and developing solutions that can keep pace with rapidly advancing technologies.

UEBA: A Smarter Way to Fight AI-Driven Cyberattacks

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UEBA

Artificial Intelligence Data Transfer Technology UEBA

UEBA: A Smarter Way to Fight AI-Driven Cyberattacks

As artificial intelligence (AI) grows, cyberattacks are becoming more advanced and harder to stop. Traditional security systems that protect company networks are no longer enough, especially when dealing with insider threats, stolen passwords, and attackers who move through systems unnoticed.

Recent studies warn that cybercriminals are using AI to make their attacks faster, smarter, and more damaging. These advanced attackers can now automate phishing emails and create malware that changes its form to avoid being caught. Some reports also show that AI is helping hackers quickly gather information and launch more targeted, widespread attacks.

To fight back, many security teams are now using a more intelligent system called User and Entity Behavior Analytics (UEBA). Instead of focusing only on known attack patterns, UEBA carefully tracks how users normally behave and quickly spots unusual activity that could signal a security problem.

How UEBA Works

Older security tools were based on fixed rules and could only catch threats that had already been seen before. They often missed new or hidden attacks, especially when hackers used AI to disguise their moves.

UEBA changed the game by focusing on user behavior. It looks for sudden changes in the way people or systems normally act, which may point to a stolen account or an insider threat.

Today, UEBA uses machine learning to process huge amounts of data and recognize even small changes in behavior that may be too complex for traditional tools to catch.

Key Parts of UEBA

A typical UEBA system has four main steps:

1. Gathering Data: UEBA collects information from many places, including login records, security tools, VPNs, cloud services, and activity logs from different devices and applications.

2. Setting Normal Behavior: The system learns what is "normal" for each user or system—such as usual login times, commonly used apps, or regular network activities.

3. Spotting Unusual Activity: UEBA compares new actions to normal patterns. It uses smart techniques to see if anything looks strange or risky and gives each unusual event a risk score based on its severity.

4. Responding to Risks: When something suspicious is found, the system can trigger alerts or take quick action like locking an account, isolating a device, or asking for extra security checks.

This approach helps security teams respond faster and more accurately to threats.

Why UEBA Matters

UEBA is especially useful in protecting sensitive information and managing user identities. It can quickly detect unusual activities like unexpected data transfers or access from strange locations.

When used with identity management tools, UEBA can make access control smarter, allowing easy entry for low-risk users, asking for extra verification for medium risks, or blocking dangerous activities in real time.

Challenges in Using UEBA

While UEBA is a powerful tool, it comes with some difficulties. Companies need to collect data from many sources, which can be tricky if their systems are outdated or spread out. Also, building reliable "normal" behavior patterns can be hard in busy workplaces where people’s routines often change. This can lead to false alarms, especially in the early stages of using UEBA.

Despite these challenges, UEBA is becoming an important part of modern cybersecurity strategies.

AI Integration Raises Alarms Over Enterprise Data Safety

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Web Services

AI integration Artificial Intelligence Cyber Crime CyberCrime CyberThreat Cyersecurity AI Data Data Protectoin Technology Google LLM. Okta Web Services

AI Integration Raises Alarms Over Enterprise Data Safety

Today's digital landscape has become increasingly interconnected, and cyber threats have risen in sophistication, which has significantly weakened the effectiveness of traditional security protocols. Cybercriminals have evolved their tactics to exploit emerging vulnerabilities, launch highly targeted attacks, and utilise advanced techniques to breach security perimeters to gain access to and store large amounts of sensitive and mission-critical data, as enterprises continue to generate and store significant volumes of sensitive data.

In light of this rapidly evolving threat environment, organisations are increasingly forced to adopt more adaptive and intelligent security solutions in addition to conventional defences. In the field of cybersecurity, artificial intelligence (AI) has emerged as a significant force, particularly in the area of data protection.

AI-powered data security frameworks are revolutionising the way threats are detected, analysed, and mitigated in real time, making it a transformative force. This solution enhances visibility across complex IT ecosystems, automates threat detection processes, and supports rapid response capabilities by identifying patterns and anomalies that might go unnoticed by human analysts.

Additionally, artificial intelligence-driven systems allow organisations to develop risk mitigation strategies that are scalable as well as aligned with their business objectives while implementing risk-based mitigation strategies. The integration of artificial intelligence plays a crucial role in maintaining regulatory compliance in an era where data protection laws are becoming increasingly stringent, in addition to threat prevention.

By continuously monitoring and assessing cybersecurity postures, artificial intelligence is able to assist businesses in upholding industry standards, minimising operations interruptions, and strengthening stakeholder confidence. Modern enterprises need to recognise that AI-enabled data security is no longer a strategic advantage, but rather a fundamental requirement for safeguarding digital assets in a modern enterprise, as the cyber threat landscape continues to evolve.

Varonis has recently revealed that 99% of organisations have their sensitive data exposed to artificial intelligence systems, a shocking finding that illustrates the importance of data-centric security. There has been a significant increase in the use of artificial intelligence tools in business operations over the past decade. The State of Data Security: Quantifying Artificial Intelligence's Impact on Data Risk presents an in-depth analysis of how misconfigured settings, excessive access rights and neglected security gaps are leaving critical enterprise data vulnerable to AI-driven exploitation.

An important characteristic of this report is that it relies on extensive empirical analysis rather than opinion surveys. In order to evaluate the risk associated with data across 1,000 organisations, Varonis conducted a comprehensive analysis of data across a variety of cloud computing environments, including the use of over 10 billion cloud assets and over 20 petabytes of sensitive data.

Among them were platforms such as Amazon Web Services, Google Cloud Services, Microsoft Azure Services, Microsoft 365 Services, Salesforce, Snowflake, Okta, Databricks, Slack, Zoom, and Box, which provided a broad and realistic picture of enterprise data exposure in the age of Artificial Intelligence. The CEO, President, and Co-Founder of Varonis, Yaaki Faitelson, stressed the importance of balancing innovation with risk, noting that, even though AI is undeniable in increasing productivity, it also poses serious security issues.

Due to the growing pressure on CIOs and CISOs to adopt artificial intelligence technologies at a rapid rate, advanced data security platforms are in increasing demand. It is important to take a proactive, data-oriented approach to cybersecurity to prevent AI from becoming a gateway to large-scale data breaches, says Faitelson. It is important to note that researchers are also exploring two critical dimensions of risk as they relate to large language models (LLMs) as well as AI copilots: human-to-machine interaction and machine-to-machine integrity, which are both critical aspects of risk pertaining to AI-driven data exposure.

A key focus of the study was on how sensitive data, such as employee compensation details, intellectual property rights, proprietary software, and confidential research and development insights able to be unintentionally accessed, leaked, or misused by using just a single prompt into an artificial intelligence interface if it is not protected. As AI assistants are being increasingly used throughout departments, the risk of inadvertently disclosing critical business information has increased considerably.

Additionally, two categories of risk should be addressed: the integrity and trustworthiness of the data used to train or enhance artificial intelligence systems. It is common for machine-to-machine vulnerabilities to arise when flawed, biased, or deliberately manipulated datasets are introduced into the learning cycle of machine learning algorithms.

As a consequence of such corrupted data, it can result in far-reaching and potentially dangerous consequences. For example, inaccurate or falsified clinical information could lead to life-saving medical treatments being developed, while malicious actors may embed harmful code within AI training pipelines, introducing backdoors or vulnerabilities to applications that aren't immediately detected at first.

The dual-risk framework emphasises the importance of tackling artificial intelligence security holistically, one that takes into account the entire lifecycle of data, from acquisition and input to training and deployment, not just the user-level controls. Considering both human-induced and systemic risks associated with generative AI tools, organisations can implement more resilient safeguards to ensure that their most valuable data assets are protected as much as possible.

Organisations should reconsider and go beyond conventional governance models to secure sensitive data in the age of AI. In an environment where AI systems require dynamic, expansive access to vast datasets, traditional approaches to data protection -often rooted in static policies and role-based access -are no longer sufficient.

Towards the future of AI-ready security, a critical balance must be struck between ensuring robust protection against misuse, leakage, and regulatory non-compliance, while simultaneously enabling data access for innovation. Organisations need to adopt a multilayered, forward-thinking security strategy customised for AI ecosystems to meet these challenges.

It is important to note that some key components of a data-tagging and classification strategy are the identification and categorisation of sensitive information to determine how it should be handled depending on the criticality of the information. As a replacement for role-based access control (RBAC), attribute-based access control (ABAC) should allow for more granular access policies based on the identity of the user, context, and the sensitivity of the data.

Aside from that, organisations need to design data pipelines that are AI-aware and incorporate proactive security checkpoints into them so as to monitor how their data is used by artificial intelligence tools. Additionally, output validation becomes crucial—it involves implementing mechanisms that ensure outputs generated by artificial intelligence are compliant, accurate, and potentially risky before they are circulated internally or externally.

The complexity of this landscape has only been compounded by the rise of global regulations and regional regulations that govern data protection and artificial intelligence. In addition to the general data privacy frameworks of GDPR and CCPA, businesses will now need to prepare themselves for emerging AI-specific regulations that will put a stronger emphasis on how AI systems access and process sensitive data. As a result of this regulatory evolution, organisations need to maintain a security posture that is both agile and anticipatable.

Matillion Data Productivity Cloud, for instance, is a solution that embodies this principle of "secure by design". As a hybrid cloud SaaS platform tailored to enterprise environments, Matillion has created a platform that is well-suited to secure enterprise environments.

With its standardised encryption and authentiyoucation protocols, the platform is easily integrated into enterprise networks through the use of a secure cloud infrastructure. This platform is built around a pushdown architecture that prevents customer data from leaving the organisation's own cloud environment while allowing advanced orchestration of complex data workflows in order to minimise the risk of data exposure.

Rather than focusing on data movement, Matillion's focus is on metadata management and workflow automation, providing organisations with a secure, efficient data operation, allowing them to gain insights faster with a higher level of data integrity and compliance. Organisations must move towards a paradigm shift—where security is woven into the fabric of the data lifecycle—as AI poses a dual pressure on organisations.

A shift from traditional governance systems to more adaptive, intelligent frameworks will help secure data in the AI era. Because AI systems require broad access to enterprise data, organisations must strike a balance between openness and security. To achieve this, data can be tagged and classified and attributes can be used to manage access precisely, attribute-based access controls should be implemented for precise control of access, and AI-aware data pipelines must be built with security checks, and output validation must be performed to prevent the distribution of risky or non-compliant AI-generated results.

With the rise of global and AI-specific regulations, companies need to develop compliance strategies that will ensure future success. Matillion Data Productivity Cloud is an example of a platform which offers a secure-by-design solution, as it combines a hybrid SaaS architecture with enterprise-grade security and security controls.

Through its pushdown processing, the customer's data will stay within the organisation's cloud environment while the workflows are orchestrated safely and efficiently. In this way, organisations can make use of AI confidently without sacrificing data security or compliance with the laws and regulations. As artificial intelligence and enterprise data security rapidly evolve, organisations need to adopt a future-oriented mindset that emphasises agility, responsibility, and innovation.

It is no longer possible to rely on reactive cybersecurity; instead, businesses must embrace AI-literate governance models, advance threat intelligence capabilities, and secure infrastructures designed with security in mind. Data security must be embedded into all phases of the data lifecycle, from creation and classification to accessing, analysing, and transforming it with AI. Developing a culture of continuous risk evaluation is a must for leadership teams, and IT and data teams must be empowered to collaborate with compliance, legal, and business units proactively.

In order to maintain trust and accountability, it will be imperative to implement clear policies regarding AI usage, ensure traceability in data workflows, and establish real-time auditability. Further, with the maturation of AI regulations and the increasing demands for compliance across a variety of sectors, forward-looking organisations should begin aligning their operational standards with global best practices rather than waiting for mandatory regulations to be passed.

A key component of artificial intelligence is data, and the protection of that foundation is a strategic imperative as well as a technical obligation. By putting the emphasis on resilient, ethical, and intelligent data security, today's companies will not only mitigate risk but will also be able to reap the full potential of AI tomorrow.

Best Practices for SOC Threat Intelligence Integration

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Threat Intelligence

Artificial Intelligence Business Security Cyber Security SOC Threat Intelligence

Best Practices for SOC Threat Intelligence Integration

As cyber threats become more complex and widespread, Security Operations Centres (SOCs) increasingly rely on threat intelligence to transform their defensive methods from reactive to proactive. Integrating Cyber Threat Intelligence (CTI) into SOC procedures has become critical for organisations seeking to anticipate attacks, prioritise warnings, and respond accurately to incidents.

This transition is being driven by the increasing frequency of cyberattacks, particularly in sectors such as manufacturing and finance. Adversaries use old systems and heterogeneous work settings to spread ransomware, phishing attacks, and advanced persistent threats (APTs).

Importance of threat intelligence in modern SOCs

Threat intelligence provides SOCs with contextualised data on new threats, attacker strategies, and vulnerabilities. SOC teams can discover patterns and predict possible attack vectors by analysing indications of compromise (IOCs), tactics, methods, and procedures (TTPs), and campaign-specific information.

For example, the MITRE ATT&CK framework has become a key tool for mapping adversary behaviours, allowing SOCs to practice attacks and improve detection techniques. According to a recent industry research, organisations that integrated CTI into their Security Information and Event Management (SIEM) systems reduced mean dwell time, during which attackers went undetected, by 78%.

Accelerating the response to incidents

Threat intelligence allows SOCs to move from human triage to automated response workflows. Security Orchestration, Automation, and Response (SOAR) platforms run pre-defined playbooks for typical attack scenarios such as phishing and ransomware. When a multinational retailer automated IOC blocklisting, reaction times were cut from hours to seconds, preventing potential breaches and data exfiltration.

Furthermore, threat intelligence sharing consortiums, such as sector-specific Information Sharing and Analysis Centres (ISACs), enable organisations to pool anonymised data. This partnership has effectively disrupted cross-industry efforts, including a recent ransomware attack on healthcare providers.

Proactive threat hunting

Advanced SOCs are taking a proactive approach, performing regular threat hunts based on intelligence-led hypotheses. Using adversary playbooks and dark web monitoring, analysts find stealthy threats that avoid traditional detection. A technology firm's SOC team recently discovered a supply chain threat by linking vendor vulnerabilities to dark web conversation about a planned hack.

Purple team exercises—simulated attacks incorporating red and blue team tactics—have also gained popularity. These drills, based on real-world threat data, assess SOC readiness for advanced persistent threats. Organisations who perform quarterly purple team exercises report a 60% increase in incident control rates.

AI SOCs future

Artificial intelligence (AI) is poised to transform threat intelligence. Natural language processing (NLP) technologies can now extract TTPs from unstructured threat data and generate SIEM detection rules automatically.

During beta testing, these technologies cut rule creation time from days to minutes. Collaborative defence models are also emerging. National and multinational programs, such as INTERPOL's Global Cybercrime Program, help to facilitate cross-border intelligence exchange.

A recent operation involving 12 countries successfully removed a botnet responsible for $200 million in financial fraud, demonstrating the potential of collective defence.

Cerebras Unveils World’s Fastest AI Chip, Beating Nvidia in Inference Speed

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Cerebras Unveils World’s Fastest AI Chip, Beating Nvidia in Inference Speed

In a move that could redefine AI infrastructure, Cerebras Systems showcased its record-breaking Wafer Scale Engine (WSE) chip at Web Summit Vancouver, claiming it now holds the title of the world’s fastest AI inference engine.

Roughly the size of a dinner plate, the latest WSE chip spans 8.5 inches (22 cm) per side and packs an astonishing 4 trillion transistors — a monumental leap from traditional processors like Intel’s Core i9 (33.5 billion transistors) or Apple’s M2 Max (67 billion).

The result? A groundbreaking 2,500 tokens per second on Meta’s Llama 4 model, nearly 2.5 times faster than Nvidia’s recently announced benchmark of 1,000 tokens per second. “Inference is where speed matters the most,” said Naor Penso, Chief Information Security Officer at Cerebras. “Last week Nvidia hit 1,000 tokens per second — which is impressive — but today, we’ve surpassed that with 2,500 tokens per second.”

Inference refers to how AI processes information to generate outputs like text, images, or decisions. Tokens, which can be words or characters, represent the basic units AI uses to interpret and respond. As AI agents take on more complex, multi-step tasks, inference speed becomes increasingly essential. “Agents need to break large tasks into dozens of sub-tasks and communicate between them quickly,” Penso explained. “Slow inference disrupts that entire flow.”

What sets Cerebras apart isn’t just transistor count — it’s the chip’s design. Unlike Nvidia GPUs that require off-chip memory access, WSE integrates 44GB of high-speed RAM directly on-chip, ensuring ultra-fast data access and reduced latency. Independent benchmarks back Cerebras’ claims.

Artificial Analysis, a third-party testing agency, confirmed the WSE achieved 2,522 tokens per second on Llama 4, outperforming Nvidia’s new Blackwell GPU (1,038 tokens/sec). “Cerebras is the only inference solution that currently outpaces Blackwell for Meta’s flagship model,” said Artificial Analysis CEO Micah Hill-Smith.

While CPUs and GPUs have driven AI advancements for decades, Cerebras’ WSE represents a shift toward a new compute paradigm. “This isn’t x86 or ARM, It’s a new architecture designed to supercharge AI workloads,” said Julie Shin, Chief Marketing Officer at Cerebras.

Technology Meets Therapy as AI Enters the Conversation

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Therapy

AI based Therapy AI Theraphy AI Threat AI-Healthcare system Artificial Intelligence ChatGPT CyberCrime CyberThreat mental Health Technology Therapy

Technology Meets Therapy as AI Enters the Conversation

Several studies show that artificial intelligence has become an integral part of mental health care, changing the way practitioners deliver, document, and conceptualise therapy over the years, as well as how professionals are implementing, documenting, and even conceptualising it. Psychiatrists associated with the American Psychiatric Association were found to be increasingly relying on artificial intelligence tools such as ChatGPT, according to a 2023 study.

In general, 44% of respondents reported that they were using the language model version 3.5, and 33% had been trying out version 4.0, which is mainly used to answer clinical questions. The study also found that 70% of people surveyed believe that AI improves or has the potential to improve the efficiency of clinical documentation. The results of a separate study conducted by PsychologyJobs.com indicated that one in four psychologists had already begun integrating artificial intelligence into their practice, and another 20% were considering the idea of adopting the technology soon.

AI-powered chatbots for client communication, automated diagnostics to support advanced treatment planning and natural language processing tools to analyse text data from patients were among the most common applications. As both studies pointed out, even though the enthusiasm for artificial intelligence is growing, there has also been a concern raised about the ethical, practical, and emotional implications of incorporating it into therapeutic settings, which has been expressed by many mental health professionals.

Therapy has traditionally been viewed as an extremely personal process that involves introspection, emotional healing, and gradual self-awareness as part of a process that is deeply personal. Individuals are provided with a structured, empathetic environment in which they can explore their beliefs, behaviours, and thoughts with the assistance of a professional. However, the advent of artificial intelligence, which is beginning to reshape the contours of this experience, is changing the shape of this experience.

It has now become apparent that ChatGPT is positioned as a complementary support in the therapeutic journey, providing continuity between sessions and enabling clients to work on their emotional work outside the therapy room. The inclusion of these tools ethically and thoughtfully can enhance therapeutic outcomes when they are implemented in a manner that reinforces key insights, encourages consistent reflection, and provides prompts that are aligned with the themes explored during formal sessions.

It is important to understand that the most valuable contribution AI has to offer in this context is that it is able to facilitate insight, enabling users to gain a clearer understanding of how people behave and feel. The concept of insight refers to the ability to move beyond superficial awareness into the identification of psychological problems that arise from psychological conditions.

One way to recognise one's tendency to withdraw during times of conflict is to recognise that it is a fear of emotional vulnerability rooted in past experiences, so understanding that this is a deeper level of self-awareness that can change life. This sort of breakthrough may often happen during therapy sessions, but it often evolves and crystallises outside the session, as a client revisits a discussion with their therapist or is confronted with a situation in their daily lives that brings new clarity to them.

AI tools can be an effective companion in these moments. This therapy extends the therapeutic process beyond the confines of scheduled appointments by providing reflective dialogue, gentle questioning, and cognitive reframing techniques to help individuals connect the dots. It is widely understood that the term "AI therapy" entails a range of technology-driven approaches that aim to enhance or support the delivery of mental health care.

At its essence, it refers to the application of artificial intelligence in therapeutic contexts, with tools designed to support licensed clinicians, as well as fully autonomous platforms that interact directly with their users. It is commonly understood that artificial intelligence-assisted therapy augments the work of human therapists with features such as chatbots that assist clients in practicing coping mechanisms, mood monitoring software that can be used to monitor mood patterns over time, and data analytics tools that provide clinicians with a better understanding of the behavior of their clients and the progression of their treatment.

In order to optimise and personalise the therapeutic process, these technologies are not meant to replace mental health professionals, but rather to empower them. On the other hand, full-service AI-driven interventions represent a more self-sufficient model of care in which users can interact directly with digital platforms without any interaction with a human therapist, leading to a more independent model of care.

Through sophisticated algorithms, these systems will be able to deliver guided cognitivbehaviouralal therapy (CBT) exercises, mindfulness practices, or structured journaling prompts tailored to fit the user's individual needs. Whether AI-based therapy is assisted or autonomous, AI-based therapy has a number of advantages, including the potential to make mental health support more accessible and affordable for individuals and families.

There are many reasons why traditional therapy is out of reach, including high costs, long wait lists, and a shortage of licensed professionals, especially in rural areas or areas that are underserved. Several logistical and financial barriers can be eliminated from the healthcare system by using AI solutions to offer care through mobile apps and virtual platforms.

It is essential to note that these tools may not completely replace human therapists when dealing with complex or crisis situations, but they significantly increase the accessibility of psychological care, enabling individuals to seek help despite facing an otherwise insurmountable barrier to accessing it. Since the advent of increased awareness of mental health, reduced stigma, and the psychological toll of global crises, the demand for mental health services has increased dramatically in recent years.

Nevertheless, there has not been an adequate number of qualified mental health professionals available, which has left millions of people with inadequate mental health care. As part of this context, artificial intelligence has emerged as a powerful tool in bridging the gap between need and accessibility. With the capability of enhancing clinicians' work as well as streamlining key processes, artificial intelligence has the potential to significantly expand mental health systems' capacity in the world. This concept, which was once thought to be futuristic, is now becoming a practical reality.

There is no doubt that artificial intelligence technologies are already transforming clinical workflows and therapeutic approaches, according to trends reported by the American Psychological Association Monitor. AI is changing how mental healthcare is delivered at every stage of the process, from intelligent chatbots to algorithms that automate administrative tasks, so that every stage of the process can be transformed by it.

A therapist who integrates AI into his/her practice can not only increase efficiency, but they can also improve the quality and consistency of the care they provide their patients with The current AI toolbox offers a wide range of applications that will support both clinical and operational functions of a therapist:

1. Assessment and Screening

It has been determined that advanced natural language processing models are being used to analyse patient speech and written communications to identify early signs of psychological distress, including suicidal ideation, severe mood fluctuations, or trauma-related triggers that may indicate psychological distress. It is possible to prevent crises before they escalate by utilising these tools, which facilitate early detection and timely intervention.

2. Intervention and Self-Help

With the help of artificial intelligence-powered chatbots built around cognitive behavioural therapy (CBT) frameworks, users can access structured mental health support at their convenience, anytime, anywhere. There is a growing body of research that suggests that these interventions can result in measurable reductions in the symptoms of depression, particularly major depressive disorder (MDD), often serving as an effective alternative to conventional treatment in treating such conditions. Recent randomised controlled trials support this claim.

3. Administrative Support

Several tasks, often a burden and time-consuming part of clinical work, are being streamlined through the use of AI tools, including drafting progress notes, assisting with diagnostic coding, and managing insurance pre-authorisation requests. As a result of these efficiencies, clinician workload and burnout are reduced, which leads to more time and energy available to care for patients.

4. Training and Supervision

The creation of standardised patients by artificial intelligence offers a revolutionary approach to clinical training. In a controlled environment, these realistic virtual clients provide therapists who are in training the opportunity to practice therapeutic techniques. Additionally, AI-based analytics can be used to evaluate session quality and provide constructive feedback to clinicians, helping them improve their skills and improve their overall treatment outcomes.

Artificial intelligence is continuously evolving, and mental health professionals must stay on top of its developments, evaluate its clinical validity, and consider the ethical implications of their use as it continues to evolve. Using AI properly can serve as a support system and a catalyst for innovation, ultimately leading to a greater reach and effectiveness of modern mental healthcare services.

As artificial intelligence (AI) is becoming increasingly popular in the field of mental health, talk therapy powered by artificial intelligence is a significant innovation that offers practical, accessible support to individuals dealing with common psychological challenges like anxiety, depression, and stress. These systems are based on interactive platforms and mobile apps, and they offer personalized coping strategies, mood tracking, and guided therapeutic exercises via interactive platforms and mobile apps.

In addition to promoting continuity of care, AI tools also assist individuals in maintaining therapeutic momentum between sessions, instead of traditional services, when access to traditional services is limited, by allowing them to access support on demand. As a result, AI interventions are more and more considered complementary to traditional psychotherapy, rather than replacing it altogether. These systems combine evidence-based techniques with those of cognitive behavioural therapy (CBT) and dialectical behaviour therapy (DBT) to provide evidence-based techniques.

With the development of these techniques into digital formats, users can engage with strategies aimed at regulating emotions, reframing cognitive events, and engaging in behavioural activation in real-time. These tools have been designed to be immediately action-oriented, enabling users to apply therapeutic principles directly to real-life situations as they arise, resulting in greater self-awareness and resilience as a result.

A person who is dealing with social anxiety, for example, can use an artificial intelligence (AI) simulation to gradually practice social interactions in a low-pressure environment, thereby building their confidence in these situations. As well, individuals who are experiencing acute stress can benefit from being able to access mindfulness prompts and reminders that will help them regain focus and ground themselves. This is a set of tools that are developed based on the clinical expertise of mental health professionals, but are designed to be integrated into everyday life, providing a scalable extension of traditional care models.

However, while AI is being increasingly utilised in therapy, it is not without significant challenges and limitations. One of the most commonly cited concerns is that there is no real sense of human interaction with the patient. The foundations of effective psychotherapy include empathy, intuition, and emotional nuance, qualities which artificial intelligence is unable to fully replicate, despite advances in natural language processing and sentiment analysis.

AI interactions can be deemed impersonal or insufficient by users seeking deeper relational support, leading to feelings of isolation or dissatisfaction in the user. Additionally, AI systems may be unable to interpret complex emotions or cultural nuances, so their responses may not have the appropriate sensitivity or relevance to offer meaningful support.

In the field of mental health applications, privacy is another major concern that needs to be addressed. These applications frequently handle highly sensitive data about their users, which makes data security an extremely important issue. Because of concerns over how their personal data is stored, managed, or possibly shared with third parties, users may not be willing to interact with these platforms.

As a result of the high level of transparency and encryption that developers and providers of AI therapy must maintain in order to gain widespread trust and legitimacy, they must also comply with privacy laws like HIPAA or GDPR to maintain a high level of legitimacy and trust.

Additionally, ethical concerns arise when algorithms are used to make decisions in deeply personal areas. As a result of the use of artificial intelligence, biases can be reinforced unintentionally, complex issues can be oversimplified, and standardised advice is provided that doesn't reflect the unique context of each individual.

In an industry that places a high value on personalisation, it is especially dangerous that generic or inappropriate responses occur. For AI therapy to be ethically sound, it must have rigorous oversight, continuous evaluation of system outputs, as well as clear guidelines to govern the proper use and limitations of these technologies. In the end, while AI presents several promising tools for extending mental health care, its success depends upon its implementation, in which innovation, accuracy, and respect for individual experience are balanced with compassion, accuracy, and respect for individuality.

When artificial intelligence is being incorporated into mental health care at an increasing pace, it is imperative that mental health professionals, policy makers, developers, and educators work together to create a framework to ensure that the application is conducted responsibly. It is not enough to have technological advances in the field of AI therapy to ensure its future, but it is also important to have a commitment to ethical responsibility, clinical integrity, and human-centred care in the industry.

A major part of ensuring that AI solutions are both safe and therapeutically meaningful will be robust research, inclusive algorithm development, and extensive clinician training. Furthermore, it is critical to maintain transparency with users regarding the capabilities and limitations of these tools so that individuals can make informed decisions regarding their mental health care.

These organisations and practitioners who wish to remain at the forefront of innovation should prioritise strategic implementation, where AI is not viewed as a replacement but rather as a valuable partner in care rather than merely as a replacement. Considering the potential benefits of integrating innovation with empathy in the mental health sector, people can make use of AI's full potential to design a more accessible, efficient, and personalised future of therapy-one in which technology amplifies the importance of human connection rather than taking it away.

Google Unveils AI With Deep Reasoning and Creative Video Capabilities

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Software

Advanced Technology Artificial Intelligence Cyber Security cybercriminals CyberThreat Google Google Ads Google Marketing Software

Google Unveils AI With Deep Reasoning and Creative Video Capabilities

This week, Google, as part of its annual Google Marketing Live 2025 event, unveiled a comprehensive suite of artificial intelligence-powered tools to help the company cement its position at the forefront of digital commerce and advertising on Wednesday, May 21, at a press conference.

Google's new tools are intended to revolutionise the way brands engage with consumers and drive measurable growth through artificial intelligence, and they are part of a strategic push that Google is making to redefine the future of advertising and online shopping. In her presentation, Vidhya Srinivasan, Vice President and General Manager of Google Ads and Commerce, stressed the importance of this change, saying, “The future of advertising is already here, fueled by artificial intelligence.”

This declaration was followed by Google's announcement of advanced solutions that will enable businesses to use smarter bidding, dynamic creative creation, and intelligent, agent-based assistants in real-time, which can adjust to user behaviour and market conditions, as well as adapt to changing market conditions. Google has launched this major product at a critical time in its history, as generative AI platforms and conversational search tools are putting unprecedented pressure on traditional search and shopping channels, diverting users away from these methods.

By leveraging technological disruptions as an opportunity for brands and marketers around the world, Google underscores its commitment to staying ahead of the curve by creating innovation-driven opportunities for brands and marketers. A long time ago, Google began to explore artificial intelligence, and since its inception in 1998, it has evolved steadily. Google’s journey into artificial intelligence dates back much earlier than many people think.

While Google has always been known for its groundbreaking PageRank algorithm, its formal commitment to artificial intelligence accelerated throughout the mid-2000s when key milestones like the acquisition of Pyra Labs in 2003 and the launch of Google Translate in 2006 were key milestones. It is these early efforts that laid the foundation for analysing content and translating it using AI. It was not long before Google Instant was introduced in 2010 as an example of how predictive algorithms were enhancing user experience by providing real-time search query suggestions.

In the years that followed, artificial intelligence research and innovation became increasingly important, as evidenced by Google X's establishment in 2011 and DeepMind's strategic acquisition in 2014, pioneers in reinforcement learning that created the historic algorithm AlphaGo. A new wave of artificial intelligence has been sweeping across the globe since 2016 with Google Assistant and advanced tools like TensorFlow, which have democratized machine learning development.

Breakthroughs such as Duplex have highlighted AI's increasing conversational sophistication, but most recently, Google's AI has embraced multimodal capabilities, which is why models like BERT, LaMDA, and PaLM are revolutionising language understanding and dialogue in a way previously unknown to the world. AI has a rich legacy that underscores its crucial role in driving Google’s transformation across search, creativity, and business solutions, underpinned by this legacy.

As part of its annual developer conference in 2025, Google I/O reaffirmed its leadership in the rapidly developing field of artificial intelligence by unveiling an impressive lineup of innovations that promise to revolutionize the way people interact with technology, reaffirming its leadership in this field. In addition to putting a heavy emphasis on artificial intelligence-driven transformation, this year's event showcased next-generation models and tools that are far superior to the ones displayed in previous years.

Among the announcements made by AI are the addition of AI assistants with deeper contextual intelligence, to the creation of entire videos with dialogue, which highlights a monumental leap forward in both the creative and cognitive capabilities of AI in general. It was this technological display that was most highlighted by the unveiling of Gemini 2.5, Google's most advanced artificial intelligence model. This model is positioned as the flagship model of the Gemini series, setting new industry standards for outstanding performance across key dimensions, such as reasoning, speed, and contextual awareness, which is among the most important elements of the model.

The Gemini 2.5 model has outperformed its predecessors and rivals, including Google's own Gemini Flash, which has redefined expectations for what artificial intelligence can do. Among the model's most significant advantages is its enhanced problem-solving ability, which makes it far more than just a tool for retrieving information; it is also a true cognitive assistant because it provides precise, contextually-aware responses to complex and layered queries.

It has significantly enhanced capabilities, but it operates at a faster pace and with better efficiency, which makes it easier to integrate into real-time applications, from customer support to high-level planning tools, seamlessly. Additionally, the model's advanced understanding of contextual cues allows it to conduct intelligent, more coherent conversations, allowing it to feel more like a human being collaborating rather than interacting with a machine. This development marks a paradigm shift in artificial intelligence in addition to incremental improvements.

It is a sign that artificial intelligence is moving toward a point where systems are capable of reasoning, adapting, and contributing in meaningful ways across the creative, technical, and commercial spheres. Google I/O 2025 serves as a preview of a future where AI will become an integral part of productivity, innovation, and experience design for digital creators, businesses, and developers alike.

Google has announced that it is adding major improvements to its Gemini large language model lineup, which marks another major step forward in Google's quest to develop more powerful, adaptive artificial intelligence systems, building on the momentum of its breakthroughs in artificial intelligence. The new iterations, Gemini 2.5 Flash and Gemini 2.5 Pro, feature significant architectural improvements that aim to optimise performance across a wide range of uses.

It will be available in early June 2025 in general availability as Gemini 2.5 Flash, a fast and lightweight processor designed for high-speed and lightweight use, and the more advanced Pro version will appear shortly afterwards as well. Among the most notable features of the Pro model is the introduction of “Deep Think” which provides advanced reasoning techniques to handle complex tasks using parallel processing techniques to handle complex issues.

As a result of its inspiration from AlphaGo's strategic modelling, Deep Think gives AI the ability to simultaneously explore various solution paths, producing faster and more accurate results. With this capability, the model is well-positioned to offer a cutting-edge solution for reasoning at the highest level, mathematical analysis, and programming that meets the demands of competition. When Demiss Hassabis, CEO of Google DeepMind, held a press briefing to highlight the model's breakthrough performance, he highlighted its impressive performance on the USAMO 2025, a challenging math challenge that is a challenging one in the world, and LiveCodeBench, another benchmark that is a popular one in advanced coding.

A statement by Hassabis said, “Deep Think pushed the performance of models to the limit, resulting in groundbreaking results.” Google is adopting a cautious release strategy to comply with its commitment to ethical AI deployment. In order to ensure safety, reliability, and transparency, Deep Think will initially be accessible only to a limited number of trusted testers who will be able to provide feedback.

In addition to demonstrating Google's intent to responsibly scale frontier AI capabilities, this deliberate rollout emphasises the importance of maintaining trust and control while showcasing the company's commitment to it. In addition to its creative AI capabilities, Google announced two powerful models for generative media during its latest announcements: Veo 3 for video generation and Imagen 4. These models represent significant breakthroughs in generative media technology.

There has been a shift in artificial intelligence-assisted content creation in recent years, and these innovations provide creators with a much deeper, more immersive toolkit that allows them to tell visual and audio stories in a way that is truly remarkable in terms of realism and precision. Veo 3 represents a transformative leap in video generation technology, and for the first time, artificial intelligence-generated videos do not only comprise silent, motion-only clips anymore, but also provide a wide range of visual effects and effects.

As a result of the integration of fully synchronised audio with Veo 3, the experience felt more like a real cinematic production than a simple algorithmic output, with ambient sounds, sound effects, and even real-time dialogue between characters, as it was in the original film. "For the first time in history, we are entering into a new era of video creation," said Demis Hassabis, CEO of Google DeepMind, highlighting how both the visual fidelity and the auditory depth of the new model were highlighted. As a result of these breakthroughs, Google has developed Flow, a new AI-powered filmmaking platform exclusively for creative professionals, which integrates these breakthroughs into Flow.

Flow is Google's latest generative modelling tool that combines the most advanced models into an intuitive interface, so storytellers can design cinematic sequences with greater ease and fluidity than ever before. In Flow, the company claims it will recreate the intuitive, inspired creative process, where iteration feels effortless and ideas evolve in a way that is effortless and effortless. Flow has already been used by several filmmakers to create short films that illustrate the creative potential of the technology, combining Flow's capabilities with traditional methods to create the films.

Additionally, Imagen 4 is the latest update to Google's image generation model, offering extraordinary improvements in visual clarity, fine detail, and especially in typography and text rendering, as well as providing unparalleled advancements in visual clarity and fine detail. With these improvements, it has become a powerful tool for marketers, designers, and content creators who need to create beautiful visuals combining high-quality imagery with precise, readable text.

The Imagen 4 platform is a significant step forward in advancing the quality of visual storytelling based on artificial intelligence, whether for branding, digital campaigns, or presentations. Despite fierce competition from leading technology companies, Google has made significant advancements in autonomous artificial intelligence agents at a time when the landscape of intelligent automation is rapidly evolving.

It is no secret that Microsoft's GitHub Copilot has already demonstrated how powerful AI-driven development assistants can be, but OpenAI's CodeX platform continues to push the boundaries of what AI has to offer. It is in this context that Google introduced innovative tools like Stitch and Jules that could generate a complete website, a codebase, and a user interface automatically without any human input. These tools signal a revolution in how software developers develop and create digital content. A convergence of autonomous artificial intelligence technologies from a variety of industry giants underscores a trend towards automating increasingly complex knowledge tasks.

Through the use of these AI systemorganisationsons can respond quickly to changing market demands and evolving consumer preferences by providing real-time recommendations and dynamic adjustments. Through such responsiveness, an organisation is able to optimise operational efficiency, maximise resource utilisation, and create sustainable growth by ensuring that the company remains tightly aligned with its strategic goals. AI provides businesses with actionable insights that enable them to compete more effectively in an increasingly complex and fast-paced market place by providing actionable insights.

Aside from software and business applications, Google's AI innovations also have great potential to have a dramatic impact on the healthcare sector, where advancements in diagnostic accuracy and personalised treatment planning have the potential to greatly improve the outcomes for patients. Furthermore, improvements in the field of natural language processing and multimodal interaction models will help provide more intuitive, accessible and useful user interfaces for users from diverse backgrounds, thus reducing barriers to adoption and enabling them to make the most of technology.

In the future, when artificial intelligence becomes an integral part of today's everyday lives, its influence will be transformative, affecting industries, redefining workflows, and generating profound social effects. The fact that Google leads the way in this space not only implies a future where artificial intelligence will augment human capabilities, but it also signals the arrival of a new era of progress in science, economics, and culture as a whole.

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US imposes sanctions against North Korea

DoJ arrests accused

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Cyber insurance and ransomware

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Incentives to promote security measures

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2. Intervention and Self-Help

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