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Threat Intelligence
AI AI Tool Data Privacy Technology Threat Intelligence

AI and Privacy – Issues and Challenges

 

Artificial intelligence is changing cybersecurity and digital privacy. It promises better security but also raises concerns about ethical boundaries, data exploitation, and spying. From facial recognition software to predictive crime prevention, customers are left wondering where to draw the line between safety and overreach as AI-driven systems become more and more integrated into daily life.

The same artificial intelligence (AI) tools that aid in spotting online threats, optimising security procedures, and stopping fraud can also be used for intrusive data collecting, behavioural tracking, and mass spying. The use of AI-powered surveillance in corporate data mining, law enforcement profiling, and government tracking has drawn criticism in recent years. AI runs the potential of undermining rather than defending basic rights in the absence of clear regulations and transparency. 

AI and data ethics

Despite encouraging developments, there are numerous instances of AI-driven inventions going awry, which raise serious questions. A face recognition business called Clearview AI amassed one of the largest facial recognition databases in the world by illegally scraping billions of photos from social media. Clearview's technology was employed by governments and law enforcement organisations across the globe, leading to legal action and regulatory action about mass surveillance. 

The UK Department for Work and Pensions used an AI system to detect welfare fraud. An internal investigation suggested that the system disproportionately targeted people based on their age, handicap, marital status, and country. This prejudice resulted in certain groups being unfairly picked for fraud investigations, raising questions about discrimination and the ethical use of artificial intelligence in public services. Despite earlier guarantees of impartiality, the findings have fuelled calls for increased openness and supervision in government AI use. 

Regulations and consumer protection

The ethical use of AI is being regulated by governments worldwide, with a number of significant regulations having an immediate impact on consumers. The AI Act of the European Union, which is scheduled to go into force in 2025, divides AI applications into risk categories. 

Strict regulations will be applied to high-risk technology, like biometric surveillance and facial recognition, to guarantee transparency and moral deployment. The EU's commitment to responsible AI governance is further reinforced by the possibility of severe sanctions for non compliant companies. 

Individuals in the United States have more control over their personal data according to California's Consumer Privacy Act. Consumers have the right to know what information firms gather about them, to seek its erasure, and to opt out of data sales. This rule adds an important layer of privacy protection in an era where AI-powered data processing is becoming more common. 

The White House has recently introduced the AI Bill of Rights, a framework aimed at encouraging responsible AI practices. While not legally enforceable, it emphasises the need of privacy, transparency, and algorithmic fairness, pointing to a larger push for ethical AI development in policy making.
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Advanced Technology AI CyberCrime Cybersecurity CyberThreat malware

Attackers Exploit Click Tolerance to Deliver Malware to Users


 

The Multi-Factor Authentication (MFA) system has been a crucial component of modern cybersecurity for several years now. It is intended to enhance security by requiring additional forms of verification in addition to traditional passwords. MFA strengthens access control by integrating two or more authentication factors, which reduces the risk of credential-based attacks on the network. 

Generally, authentication factors are divided into three categories: knowledge-based factors, such as passwords or personal identification numbers (PINs); possession-based factors, such as hardware tokens sent to registered devices or one-time passcodes sent to registered devices; as well as inherent factors, such as fingerprints, facial recognition, or iris scans, which are biometric identifiers used to verify identity. Although Multi-factor authentication significantly reduces the probability that an unauthorized user will gain access to the computer, it is not entirely foolproof.

Cybercriminals continue to devise sophisticated methods to bypass authentication protocols, such as exploiting implementation gaps, exploiting technical vulnerabilities, or influencing human behaviour. With the evolution of threats, organizations need proactive security strategies to strengthen their multifactor authentication defences, making sure they remain resilient against new attack vectors. 

Researchers have recently found that cybercriminals are exploiting users' familiarity with verification procedures to deceive them into unknowingly installing malicious software on their computers. The HP Wolf Security report indicates that multiple threat campaigns have been identified in which attackers have taken advantage of the growing number of authentication challenges that users face to verify their identities, as a result of increasing the number of authentication challenges. 

The report discusses an emerging tactic known as "click tolerance" that highlights how using authentication protocols often has conditioned users to follow verification steps without thinking. Because of this, individuals are more likely to be deceptively prompted, which mimic legitimate security measures, as a result. 

Using this behavioural pattern, attackers deployed fraudulent CAPTCHAs that directed victims to malicious websites and manipulated them into accepting counterfeit authentication procedures designed to trick users into unwittingly granting them access or downloading harmful payloads. As a result of these fraudulent CAPTCHAs, attackers were able to leverage this pattern. 

For cybersecurity awareness to be effective and for security measures to be more sophisticatedtoo counter such deceptive attack strategies, heightened awareness and more sophisticated security measures are needed. A similar strategy was used in the past to steal one-time passcodes (OTPs) through the use of multi-factor authentication fatigue. The new campaign illustrates how security measures can unintentionally foster complacency in users, which is easily exploited by attackers. 

Pratt, a cybersecurity expert, states that the attack is designed to take advantage of the habitual engagement of users with authentication processes to exploit them. People are increasingly having difficulty distinguishing between legitimate security procedures and malicious attempts to deceive them, as they become accustomed to completing repetitive, often tedious verification steps. "The majority of users have become accustomed to receiving authentication prompts, which require them to complete a variety of steps to access their account. 

To verify access or to log in, many people follow these instructions without thinking about it. According to Pratt, cybercriminals are now exploiting this behaviour pattern by using fake CAPTCHAs to manipulate users into unwittingly compromising their security as a result of this behavioural pattern." As he further explained, this trend indicates a significant gap in employee cybersecurity training. Despite the widespread implementation of phishing awareness programs, many fail to adequately address what should be done once a user has fallen victim to an initial deception in the attack chain. 

To reduce the risks associated with these evolving threats, it is vital to focus training initiatives on post-compromise response strategies. When it comes to dealing with cyber threats in the age of artificial intelligence, organizations should adopt a proactive, comprehensive security strategy that will ensure that the entire digital ecosystem is protected from evolving threats. By deploying generative artificial intelligence as a force multiplier, threat detection, prevention, and response capability will be significantly enhanced. 

For cybersecurity resilience to be strengthened, the following key measures must be taken preparation, prevention, and defense. Security should begin with a comprehensive approach, utilizing Zero Trust principles to secure digital assets throughout their lifecycle, from devices to identities to infrastructure to data, cloud environments, networks, and artificial intelligence systems to secure digital assets. Taking such measures also entails safeguarding devices, identities, infrastructures, data, and networks.

To ensure robust identity verification, it is essential to use AI-powered analytics to monitor user and system behaviour to identify potential security breaches in real-time, and to identify potential security threats. To implement explicit authentication, AI-driven biometric authentication methods need to be paired with phishing-resistant protocols like Fast Identity Online (FIDO) and Multifactor Authentication (MFA) which can protect against phishing attacks. 

It has been shown that passwordless authentication increases security, and continuous identity infrastructure management – including permission oversight and removing obsolete applications – reduces vulnerability. In order to accelerate mitigation efforts, we need to implement generative artificial intelligence with Extended Detection and Response (XDR) solutions. These technologies can assist in identifying, investigating, and responding to security incidents quickly and efficiently. 

It is also critical to integrate exposure management tools with organizations' security posture to help them prevent breaches before they occur. Protecting data remains the top priority, which requires the use of enhanced security and insider risk management. Using AI-driven classification and protection mechanisms will allow sensitive data to be automatically secured across all environments, regardless of their location. It is also essential for organizations to take advantage of insider risk management tools that can identify anomalous user activities as well as data misuse, enabling timely intervention and risk mitigation. 

Organizations need to ensure robust AI security and governance frameworks are in place before implementing AI. It is imperative to conduct regular red teaming exercises to identify vulnerabilities in the system before they can be exploited by real-world attackers. An understanding of artificial intelligence applications within the organization is crucial to ensuring that AI technologies are deployed in accordance with security, privacy, and ethical standards. To maintain system integrity, updates of both software and firmware must be performed consistently. 

Automating patch management can prevent attackers from exploiting known security gaps by remediating vulnerabilities promptly. To maintain good digital hygiene, it is important not to overlook these practices. Keeping browsing data, such as users' history, cookies, and cached site information, clean reduces their exposure to online threats. Users should also avoid entering sensitive personal information on insecure websites, which is also critical to preventing online threats. Keeping digital environments secure requires proactive monitoring and threat filtering. 

The organization should ensure that advanced phishing and spam filters are implemented and that mobile devices are configured in a way that blocks malicious content on them. To enhance collective defences, the industry needs to collaborate to make these defences more effective. Microsoft Sentinel is a platform powered by artificial intelligence, which allows organizations to share threat intelligence, thus creating a unified approach to cybersecurity, which will allow organizations to be on top of emerging threats, and it is only through continuous awareness and development of skills that a strong cybersecurity culture can be achieved.

Employees must receive regular training on how to protect their assets as well as assets belonging to the organization. With an AI-enabled learning platform, employees can be upskilled and retrained to ensure they remain prepared for the ever-evolving cybersecurity landscape, through upskilling and reskilling.
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AI AI vulnerabilities API Artificial Intelligence CERT-In CyberCrime Cyberhackers CyberThreat Technology

AI as a Key Solution for Mitigating API Cybersecurity Threats

 


Artificial Intelligence (AI) is continuously evolving, and it is fundamentally changing the cybersecurity landscape, enabling organizations to mitigate vulnerabilities more effectively as a result. As artificial intelligence has improved the speed and scale with which threats can be detected and responded, it has also introduced a range of complexities that necessitate a hybrid approach to security management. 

An approach that combines traditional security frameworks with human-digital interventions is necessary. There is one of the biggest challenges AI presents to us, and that is the expansion of the attack surface for Application Programming Interfaces (APIs). The proliferation of AI-powered systems raises questions regarding API resilience as sophisticated threats become increasingly sophisticated. As AI-driven functionality is integrated into APIs, security concerns have increased, which has led to the need for robust defensive strategies. 

In the context of AI security, the implications of the technology extend beyond APIs to the very foundation of Machine Learning (ML) applications as well as large language models. Many of these models are trained on highly sensitive datasets, raising concerns about their privacy, integrity, and potential exploitation. When training data is handled improperly, unauthorized access can occur, data poisoning can occur, and model manipulation may occur, which can further increase the security vulnerability. 

It is important to note, however, that artificial intelligence is also leading security teams to refine their threat modeling strategies while simultaneously posing security challenges. Using AI's analytical capabilities, organizations can enhance their predictive capabilities, automate risk assessments, and implement smarter security frameworks that can be adapted to the changing environment. By adapting to this evolution, security professionals are forced to adopt a proactive and adaptive approach to reducing potential threats. 

Using artificial intelligence effectively while safeguarding digital assets requires an integrated approach that combines traditional security mechanisms with AI-driven security solutions. This is necessary to ensure an effective synergy between automation and human oversight. Enterprises must foster a comprehensive security posture that integrates both legacy and emerging technologies to be more resilient in the face of a changing threat landscape. However, the deployment of AI in cybersecurity requires a well-organized, strategic approach. While AI is an excellent tool for cybersecurity, it does need to be embraced in a strategic and well-organized manner. 

Building a robust and adaptive cybersecurity ecosystem requires addressing API vulnerabilities, strengthening training data security, and refining threat modeling practices. A major part of modern digital applications is APIs, allowing seamless data exchange between various systems, enabling seamless data exchange. However, the widespread adoption of APIs has also led to them becoming prime targets for cyber threats, which have put organizations at risk of significant risks, such as data breaches, financial losses, and disruptions in services.

AI platforms and tools, such as OpenAI, Google's DeepMind, and IBM's Watson, have significantly contributed to advancements in several technological fields over the years. These innovations have revolutionized natural language processing, machine learning, and autonomous systems, leading to a wide range of applications in critical areas such as healthcare, finance, and business. Consequently, organizations worldwide are turning to artificial intelligence to maximize operational efficiency, simplify processes, and unlock new growth opportunities. 

While artificial intelligence is catalyzing progress, it also introduces potential security risks. In addition to manipulating the very technologies that enable industries to orchestrate sophisticated cyber threats, cybercriminals can also use those very technologies. As a result, AI is viewed as having two characteristics: while it is possible for AI-driven security systems to proactively identify, predict, and mitigate threats with extraordinary accuracy, adversaries can weaponize such technologies to create highly advanced cyberattacks, such as phishing schemes and ransomware. 

It is important to keep in mind that, as AI continues to grow, its role in cybersecurity is becoming more complex and dynamic. Organizations need to take proactive measures to protect their organizations from AI attacks by implementing robust frameworks that harness its defensive capabilities and mitigate its vulnerabilities. For a secure digital ecosystem that fosters innovation without compromising cybersecurity, it will be crucial for AI technologies to be developed ethically and responsibly. 

The Application Programming Interface (API) is the fundamental component of digital ecosystems in the 21st century, enabling seamless interactions across industries such as mobile banking, e-commerce, and enterprise solutions. They are also a prime target for cyber-attackers due to their widespread adoption. The consequences of successful breaches can include data compromises, financial losses, and operational disruptions that can pose significant challenges to businesses as well as consumers alike. 

Pratik Shah, F5 Networks' Managing Director for India and SAARC, highlighted that APIs are an integral part of today's digital landscape. AIM reports that APIs account for nearly 90% of worldwide web traffic and that the number of public APIs has grown 460% over the past decade. Despite this rapid proliferation, the company has been exposed to a wide array of cyber risks, including broken authentication, injection attacks, and server-side request forgery. According to him, the robustness of Indian API infrastructure significantly influences India's ambitions to become a global leader in the digital industry. 

“APIs are the backbone of our digital economy, interconnecting key sectors such as finance, healthcare, e-commerce, and government services,” Shah remarked. Shah claims that during the first half of 2024, the Indian Computer Emergency Response Team (CERT-In) reported a 62% increase in API-targeted attacks. The extent of these incidents goes beyond technical breaches, and they represent substantial economic risks that threaten data integrity, business continuity, and consumer trust in addition to technological breaches.

Aside from compromising sensitive information, these incidents have also undermined business continuity and undermined consumer confidence, in addition to compromising business continuity. APIs will continue to be at the heart of digital transformation, and for that reason, ensuring robust security measures will be critical to mitigating potential threats and protecting organisational integrity. 


Indusface recently published an article on API security that underscores the seriousness of API-related threats for the next 20 years. There has been an increase of 68% in attacks on APIs compared to traditional websites in the report. Furthermore, there has been a 94% increase in Distributed Denial-of-Service (DDoS) attacks on APIs compared with the previous quarter. This represents an astounding 1,600% increase when compared with website-based DDoS attacks. 

Additionally, bot-driven attacks on APIs increased by 39%, emphasizing the need to adopt robust security measures that protect these vital digital assets from threats. As a result of Artificial Intelligence, cloud security is being transformed by enhancing threat detection, automating responses, and providing predictive insights to mitigate cyber risks. 

Several cloud providers, including Google Cloud, Microsoft, and Amazon Web Services, employ artificial intelligence-driven solutions for monitoring security events, detecting anomalies, and preventing cyberattacks.

The solutions include Chronicle, Microsoft Defender for Cloud, and Amazon GuardDuty. Although there are challenges like false positives, adversarial AI attacks, high implementation costs, and concerns about data privacy, they are still important to consider. 

Although there are still some limitations, advances in self-learning AI models, security automation, and quantum computing are expected to raise AI's profile in the cybersecurity space to a higher level. The cloud environment should be safeguarded against evolving threats by using AI-powered security solutions that can be deployed by businesses.
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AI Backdoor Data Encryption Pegasus Spyware Technology WhatsApp

Frances Proposes Law Requiring Tech Companies to Provide Encrypted Data


Law demanding companies to provide encrypted data

New proposals in the French Parliament will mandate tech companies to give decrypted messages, email. If businesses don’t comply, heavy fines will be imposed.

France has proposed a law requiring end-to-end encryption messaging apps like WhatsApp and Signal, and encrypted email services like Proton Mail to give law enforcement agencies access to decrypted data on demand. 

The move comes after France’s proposed “Narcotraffic” bill, asking tech companies to hand over encrypted chats of suspected criminals within 72 hours. 

The law has stirred debates in the tech community and civil society groups because it may lead to building of “backdoors” in encrypted devices that can be abused by threat actors and state-sponsored criminals.

Individuals failing to comply will face fines of €1.5m and companies may lose up to 2% of their annual world turnover in case they are not able to hand over encrypted communications to the government.

Criminals will exploit backdoors

Few experts believe it is not possible to bring backdoors into encrypted communications without weakening their security. 

According to Computer Weekly’s report, Matthias Pfau, CEO of Tuta Mail, a German encrypted mail provider, said, “A backdoor for the good guys only is a dangerous illusion. Weakening encryption for law enforcement inevitably creates vulnerabilities that can – and will – be exploited by cyber criminals and hostile foreign actors. This law would not just target criminals, it would destroy security for everyone.”

Researchers stress that the French proposals aren’t technically sound without “fundamentally weakening the security of messaging and email services.” Similar to the “Online Safety Act” in the UK, the proposed French law exposes a serious misunderstanding of the practical achievements with end-to-end encrypted systems. Experts believe “there are no safe backdoors into encrypted services.”

Use of spyware may be allowed

The law will allow using infamous spywares such as NSO Group’s Pegasus or Pragon that will enable officials to remotely surveil devices. “Tuta Mail has warned that if the proposals are passed, it would put France in conflict with European Union laws, and German IT security laws, including the IT Security Act and Germany’s Telecommunications Act (TKG) which require companies to secure their customer’s data,” reports Computer Weekly.

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Technology
AI Antivirus Internet Password Technology

These Four Basic PC Essentials Will Protect You From Hacking Attacks


There was a time when the internet could be considered safe, if the users were careful. Gone are the days, safe internet seems like a distant dream. It is not a user's fault when the data is leaked, passwords are compromised, and malware makes easy prey. 

Online attacks are a common thing in 2025. The rising AI use has contributed to cyberattacks with faster speed and advanced features, the change is unlikely to slow down. To help readers, this blog outlines the basics of digital safety. 

Antivirus

A good antivirus in your system helps you from malware, ransomware, phishing sites, and other major threats. 

For starters, having Microsoft’s built-in Windows Security antivirus is a must (it is usually active in the default settings, unless you have changed it). Microsoft antivirus is reliable and runs without being nosy in the background.

You can also purchase paid antivirus software, which provides an extra security and additional features, in an all-in-one single interface.

Password manager

A password manager is the spine of login security, whether an independent service, or a part of antivirus software, to protect login credentials across the web. In addition they also lower the chances of your data getting saved on the web.

A simple example: to maintain privacy, keep all the credit card info in your password manager, instead of allowing shopping websites to store sensitive details. 

You'll be comparatively safer in case a threat actor gets unauthorized access to your account and tries to scam you.

Two-factor authentication 

In today's digital world, just a standalone password isn't a safe bet to protect you from attackers. Two-factor authentication (2FA) or multi-factor authentication provides an extra security layer before users can access their account. For instance, if a hacker has your login credentials, trying to access your account, they won't have all the details for signing in. 

A safer option for users (if possible) is to use 2FA via app-generated one-time codes; these are safer than codes sent through SMS, which can be intercepted. 

Passkeys

If passwords and 2FA feel like a headache, you can use your phone or PC as a security option, through a passkey.

Passkeys are easy, fast, and simple; you don't have to remember them; you just store them on your device. Unlike passwords, passkeys are linked to the device you've saved them on, this prevents them from getting stolen or misused by hackers. You're done by just using PIN or biometric authentication to allow a passkey use.

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AI Artifical Intelligence CyberCrime Cybersecuriti CyberThreat Data data security Technology

Building Robust AI Systems with Verified Data Inputs

 


Artificial intelligence is inherently dependent on the quality of data that powers it for it to function properly. However, this reliance presents a major challenge to the development of artificial intelligence. There is a recent report that indicates that approximately half of executives do not believe their data infrastructure is adequately prepared to handle the evolving demands of artificial intelligence technologies.

As part of the study, conducted by Dun & Bradstreet, executives of companies actively integrating artificial intelligence into their business were surveyed. As a result of the survey, 54% of these executives expressed concern over the reliability and quality of their data, which was conducted on-site during the AI Summit New York, which occurred in December of 2017. Upon a broader analysis of AI-related concerns, it is evident that data governance and integrity are recurring themes.

Several key issues have been identified, including data security (46%), risks associated with data privacy breaches (43%), the possibility of exposing confidential or proprietary data (42%), as well as the role data plays in reinforcing bias in artificial intelligence models (26%) As organizations continue to integrate AI-driven solutions, the importance of ensuring that data is accurate, secure, and ethically used continues to grow. AI applications must be addressed as soon as possible to foster trust and maximize their effectiveness across industries. In today's world, companies are increasingly using artificial intelligence (AI) to enhance innovation, efficiency, and productivity. 

Therefore, ensuring the integrity and security of their data has become a critical priority for them. Using artificial intelligence to automate data processing streamlines business operations; however, it also presents inherent risks, especially in regards to data accuracy, confidentiality, and regulatory compliance. A stringent data governance framework is a critical component of ensuring the security of sensitive financial information within companies that are developing artificial intelligence. 

Developing robust management practices, conducting regular audits, and enforcing rigorous access control measures are crucial steps in safeguarding sensitive financial information in AI development companies. Businesses must remain focused on complying with regulatory requirements so as to mitigate the potential legal and financial repercussions. During business expansion, organizations may be exposed to significant vulnerabilities if they fail to maintain data integrity and security. 

As long as data protection mechanisms are reinforced and regulatory compliance is maintained, businesses will be able to minimize risks, maintain stakeholder trust, and ensure long-term success of AI-driven initiatives by ensuring compliance with regulatory requirements. As far as a variety of industries are concerned, the impact of a compromised AI system could be devastating. From a financial point of view, inaccuracies or manipulations in AI-driven decision-making, as is the case with algorithmic trading, can result in substantial losses for the company. 

Similarly, in safety-critical applications, including autonomous driving, the integrity of artificial intelligence models is directly related to human lives. When data accuracy is compromised or system reliability is compromised, catastrophic failures can occur, endangering both passengers and pedestrians at the same time. The safety of the AI-driven solutions must be maintained and trusted by ensuring robust security measures and continuous monitoring.

Experts in the field of artificial intelligence recognize that there is an insufficient amount of actionable data available to fully support the transforming landscape of artificial intelligence. Because of this scarcity of reliable data, many AI-driven initiatives have been questioned by many people as a result. As Kunju Kashalikar, Senior Director of Product Management at Pentaho points out, organizations often have difficulty seeing their data, since they do not know who owns it, where it originated from, and how it has changed. 

Lack of transparency severely undermines the confidence that users have in the capabilities of AI systems and their results. To be honest, the challenges associated with the use of unverified or unreliable data go beyond inefficiency in operations. According to Kasalikar, if data governance is lacking, proprietary information or biased information may be fed into artificial intelligence models, potentially resulting in intellectual property violations and data protection violations. Further, the absence of clear data accountability makes it difficult to comply with industry standards and regulatory frameworks when there is no clear accountability for data. 

There are several challenges faced by organizations when it comes to managing structured data. Structured data management strategies ensure seamless integration across various AI-driven projects by cataloguing data at its source in standardized, easily understandable terminology. Establishing well-defined governance and discovery frameworks will enhance the reliability of AI systems. These frameworks will also support regulatory compliance, promoting greater trust in AI applications and transparency. 

Ensuring the integrity of AI models is crucial for maintaining their security, reliability, and compliance. To ensure that these systems remain authenticated and safe from tampering or unauthorized modification, several verification techniques have been developed. Hashing and checksums enable organizations to calculate and compare hash values following the training process, allowing them to detect any discrepancies which could indicate corruption. 

Models are watermarked with unique digital signatures to verify their authenticity and prevent unauthorized modifications. In the field of simulation, simulation behavior analysis assists with identifying anomalies that could signal system integrity breaches by tracking model outputs and decision-making patterns. Using provenance tracking, a comprehensive record of all interactions, updates, and modifications is maintained, enhancing accountability and traceability. Although these verification methods have been developed over the last few decades, they remain challenging because of the rapidly evolving nature of artificial intelligence. 

As modern models are becoming more complex, especially large-scale systems with billions of parameters, integrity assessment has become increasingly challenging. Furthermore, AI's ability to learn and adapt creates a challenge in detecting unauthorized modifications from legitimate updates. Security efforts become even more challenging in decentralized deployments, such as edge computing environments, where verifying model consistency across multiple nodes is a significant issue. This problem requires implementing an advanced monitoring, authentication, and tracking framework that integrates advanced monitoring, authentication, and tracking mechanisms to deal with these challenges. 

When organizations are adopting AI at an increasingly rapid rate, they must prioritize model integrity and be equally committed to ensuring that AI deployment is ethical and secure. Effective data management is crucial for maintaining accuracy and compliance in a world where data is becoming increasingly important. 

AI plays a crucial role in maintaining entity records that are as up-to-date as possible with the use of extracting, verifying, and centralized information, thereby lowering the risk of inaccurate or outdated information being generated as a result of overuse of artificial intelligence. The advantages that can be gained by implementing an artificial intelligence-driven data management process are numerous, including increased accuracy and reduced costs through continuous data enrichment, the ability to automate data extraction and organization, and the ability to maintain regulatory compliance with the use of real-time, accurate data that is easily accessible. 

In a world where artificial intelligence is advancing at a faster rate than ever before, its ability to maintain data integrity will become of even greater importance to organizations. Organizations that leverage AI-driven solutions can make their compliance efforts stronger, optimize resources, and handle regulatory changes with confidence.
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AI ChatGPT Email Gen AI Phishing scam Technology

Dangers of AI Phishing Scam and How to Spot Them

Dangers of AI Phishing Scam and How to Spot Them

Supercharged AI phishing campaigns are extremely challenging to notice. Attackers use AI phishing scams with better grammar, structure, and spelling, to appear legit and trick the user. In this blog, we learn how to spot AI scams and avoid becoming victims. 

Checking email language

Earlier, it was easier to spot irregularities in an e-mail, all it took was one glance. As Gen AI models use flawless grammar,  it is almost impossible to find errors in your mail copy, 

Analyze the Language of the Email Carefully

In the past, one quick skim was enough to recognize something is off with an email, typically the incorrect grammar and laughable typos being the giveaways. Since scammers now use generative AI language models, most phishing messages have flawless grammar.

But there is hope. It is easier to identify Gen AI text, and keep an eye out for an unnatural flow of sentences, if everything seems to be too perfect, chances are it’s AI.

Red flags are everywhere, even mails

Though AI has made it difficult for users to find phishing scams, they show some classic behavior. The same tips apply to detect phishing emails.

In most cases, scammers mimic businesses and wish you won’t notice. For instance, instead of an official “info@members.hotstar.com” email ID, you may notice something like “info@members.hotstar-support.com.” You may also get unrequested links or attachments, which are a huge tell. URLs (mismatched) having subtle typos or extra words/letters are comparatively difficult to notice but a huge ti-off that you are on a malicious website or interacting with a fake business.

Beware of Deepfake video scams

The biggest issue these days is combating deepfakes, which are also difficult to spot. 

The attacker makes realistic video clips using photo and video prompts and uses video calling like Zoom or FaceTime to trap potential victims (especially elders and senior citizens) to give away sensitive data. 

One may think that only old people may fall for deepfakes, but due to their sophistication, even experts fall prey to them. One famous incident happened in Hong Kong, where scammers deepfake a company CFO and looted HK$200 million (roughly $25 million).

AI is advancing, and becoming stronger every day. It is a double-edged sword, both a blessing and a curse. One should tread the ethical lines carefully and hope they don’t fall to the dark side of AI.

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AI and Quantum Computing Revive Search Efforts for Missing Malaysia Airlines Flight MH370

 

A decade after the mysterious disappearance of Malaysia Airlines Flight MH370, advancements in technology are breathing new life into the search for answers. Despite extensive global investigations, the aircraft’s exact whereabouts remain unknown. However, emerging tools like artificial intelligence (AI), quantum computing, and cutting-edge underwater exploration are revolutionizing the way data is analyzed and search efforts are conducted, offering renewed hope for a breakthrough. 

AI is now at the forefront of processing and interpreting vast datasets, including satellite signals, ocean currents, and previous search findings. By identifying subtle patterns that might have gone unnoticed before, AI-driven algorithms are refining estimates of the aircraft’s possible location. 

At the same time, quantum computing is dramatically accelerating complex calculations that would take traditional systems years to complete. Researchers, including those from IBM’s Quantum Research Team, are using simulations to model how ocean currents may have dispersed MH370’s debris, leading to more accurate predictions of its final location. Underwater exploration is also taking a major leap forward with AI-equipped autonomous drones. 

These deep-sea vehicles, fitted with advanced sensors, can scan the ocean floor in unprecedented detail and access depths that were once unreachable. A new fleet of these drones is set to be deployed in the southern Indian Ocean, targeting previously difficult-to-explore regions. Meanwhile, improvements in satellite imaging are allowing analysts to reassess older data with enhanced clarity. 

High-resolution sensors and advanced real-time processing are helping experts identify potential debris that may have been missed in earlier searches. Private space firms are collaborating with global investigative teams to leverage these advancements and refine MH370’s last known trajectory. 

The renewed search efforts are the result of international cooperation, bringing together experts from aviation, oceanography, and data science to create a more comprehensive investigative approach. Aviation safety specialist Grant Quixley underscored the importance of these innovations, stating, “New technologies could finally help solve the mystery of MH370’s disappearance.” 

This fusion of expertise and cutting-edge science is making the investigation more thorough and data-driven than ever before. Beyond the ongoing search, these technological breakthroughs have far-reaching implications for the aviation industry.

AI and quantum computing are expected to transform areas such as predictive aircraft maintenance, air traffic management, and emergency response planning. Insights gained from the MH370 case may contribute to enhanced safety protocols, potentially preventing similar incidents in the future.
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