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technology modification
AI Systems AI technology CISO data security GenAI Technology technology modification

Navigating AI and GenAI: Balancing Opportunities, Risks, and Organizational Readiness

 

The rapid integration of AI and GenAI technologies within organizations has created a complex landscape, filled with both promising opportunities and significant challenges. While the potential benefits of these technologies are evident, many companies find themselves struggling with AI literacy, cautious adoption practices, and the risks associated with immature implementation. This has led to notable disruptions, particularly in the realm of security, where data threats, deepfakes, and AI misuse are becoming increasingly prevalent. 

A recent survey revealed that 16% of organizations have experienced disruptions directly linked to insufficient AI maturity. Despite recognizing the potential of AI, system administrators face significant gaps in education and organizational readiness, leading to mixed results. While AI adoption has progressed, the knowledge needed to leverage it effectively remains inadequate. This knowledge gap has decreased only slightly, with 60% of system administrators admitting to a lack of understanding of AI’s practical applications. Security risks associated with GenAI are particularly urgent, especially those related to data. 

With the increased use of AI, enterprises have reported a surge in proprietary source code being shared within GenAI applications, accounting for 46% of all documented data policy violations. This raises serious concerns about the protection of sensitive information in a rapidly evolving digital landscape. In a troubling trend, concerns about job security have led some cybersecurity teams to hide security incidents. The most alarming AI threats include GenAI model prompt hacking, data poisoning, and ransomware as a service. Additionally, 41% of respondents believe GenAI holds the most promise for addressing cyber alert fatigue, highlighting the potential for AI to both enhance and challenge security practices. 

The rapid growth of AI has also put immense pressure on CISOs, who must adapt to new security risks. A significant portion of security leaders express a lack of confidence in their workforce’s ability to identify AI-driven cyberattacks. The overwhelming majority of CISOs have admitted that the rise of AI has made them reconsider their future in the role, underscoring the need for updated policies and regulations to secure organizational systems effectively. Meanwhile, employees have increasingly breached company rules regarding GenAI use, further complicating the security landscape. 

Despite the cautious optimism surrounding AI, there is a growing concern that AI might ultimately benefit malicious actors more than the organizations trying to defend against them. As AI tools continue to evolve, organizations must navigate the fine line between innovation and security, ensuring that the integration of AI and GenAI technologies does not expose them to greater risks.
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Technology
AI Autonomous Vehicle Testing GenAI Predictive Maintenance Technology

Generative AI Set To Transform Automotive Industry

Generative AI Set To Transform Automotive Industry

For the car sector, generative AI (GenAI) has the potential to transform how automobiles run and are maintained. GenAI's ability to learn from massive volumes of data, make intelligent decisions, and improve processes makes it extremely useful in this industry.

Autonomous Vehicle Testing

One of the most significant applications of GenAI in the automotive sector is in the realm of autonomous vehicle testing. Traditional testing methods, which rely heavily on physical prototypes and real-world trials, are both time-consuming and costly. GenAI, however, offers a groundbreaking alternative. By creating detailed simulations that replicate real-world conditions, GenAI enables comprehensive testing of autonomous systems in a virtual environment. These simulations can mimic a wide range of scenarios, from adverse weather conditions to complex urban traffic patterns, ensuring that autonomous vehicles are rigorously tested before hitting the roads.

This approach not only accelerates the development cycle but also significantly reduces costs. Manufacturers can identify and address potential issues early in the design phase, minimizing the risk of costly recalls and enhancing the overall safety of autonomous vehicles.

Predictive Maintenance

Another area where GenAI is making a substantial impact is predictive maintenance. Modern vehicles are equipped with a plethora of sensors that continuously monitor various components and systems. GenAI can analyze this in-vehicle data to accurately forecast potential component failures. By identifying signs of wear and tear or impending malfunctions, GenAI enables proactive maintenance, preventing unexpected breakdowns and reducing downtime.

This predictive capability is precious for fleet operators, who can optimize their maintenance schedules and ensure their vehicles remain in peak condition. For individual car owners, it translates to fewer trips to the mechanic and a more reliable driving experience.

Enhancing User Experience

GenAI is also set to revolutionize the user experience in next-gen automotive. Advanced AI algorithms can personalize various aspects of the driving experience, from adjusting seat positions and climate control settings to recommending optimal driving routes based on real-time traffic data. By learning from user preferences and behaviors, GenAI can create a highly customized and intuitive driving environment.

Moreover, GenAI-powered virtual assistants can provide real-time assistance and support, enhancing convenience and safety. For instance, these assistants can help drivers navigate unfamiliar routes, find nearby amenities, or even diagnose minor vehicle issues on the go.

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Supreme Court
Advertising AI generated Deepfake AI threats Cyber Security GenAI McAfee Social Media Supreme Court

Supreme Court Directive Mandates Self-Declaration Certificates for Advertisements

 

In a landmark ruling, the Supreme Court of India recently directed every advertiser and advertising agency to submit a self-declaration certificate confirming that their advertisements do not make misleading claims and comply with all relevant regulatory guidelines before broadcasting or publishing. This directive stems from the case of Indian Medical Association vs Union of India. 

To enforce this directive, the Ministry of Information and Broadcasting has issued comprehensive guidelines outlining the procedure for obtaining these certificates, which became mandatory from June 18, 2024, onwards. This move is expected to significantly impact advertisers, especially those using deepfakes generated by Generative AI (GenAI) on social media platforms like Instagram, Facebook, and YouTube. The use of deepfakes in advertisements has been a growing concern. 

In a previous op-ed titled “Urgently needed: A law to protect consumers from deepfake ads,” the rising menace of deepfake ads making misleading or fraudulent claims was highlighted, emphasizing the adverse effects on consumer rights and public figures. A survey conducted by McAfee revealed that 75% of Indians encountered deepfake content, with 38% falling victim to deepfake scams, and 18% directly affected by such fraudulent schemes. Alarmingly, 57% of those targeted mistook celebrity deepfakes for genuine content. The new guidelines aim to address these issues by requiring advertisers to provide bona fide details and final versions of advertisements to support their declarations. This measure is expected to aid in identifying and locating advertisers, thus facilitating tracking once complaints are filed. 

Additionally, it empowers courts to impose substantial fines on offenders. Despite the potential benefits, industry bodies such as the Indian Internet and Mobile Association of India (IAMAI), Indian Newspaper Association (INS), and the Indian Society of Advertisers (ISA) have expressed concerns over the additional compliance burden, particularly for smaller advertisers. These bodies argue that while self-certification has merit, the process needs to be streamlined to avoid hampering legitimate advertising activities. The challenge of regulating AI-enabled deepfake ads is further complicated by the sheer volume of digital advertisements, making it difficult for regulators to review each one. 

Therefore, it is suggested that online platforms be obligated to filter out deepfake ads, leveraging their technology and resources for efficient detection. The Ministry of Electronics and Information Technology highlighted the negligence of social media intermediaries in fulfilling their due diligence obligations under the IT Rules in a March 2024 advisory. 

Although non-binding, the advisory stipulates that intermediaries must not allow unlawful content on their platforms. The Supreme Court is set to hear the matter again on July 9, 2024, when industry bodies are expected to present their views on the new guidelines. This intervention could address the shortcomings of current regulatory approaches and set a precedent for robust measures against deceptive advertising practices. 

As the country grapples with the growing threat of dark patterns in online ads, the apex court’s involvement is crucial in ensuring consumer protection and the integrity of advertising practices in India.
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Technology Threats
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The Growing Cybersecurity Concerns of Generative Artificial Intelligence

In the rapidly evolving world of technology, generative artificial intelligence (GenAI) programs are emerging as both powerful tools and significant security risks. Cybersecurity researchers have long warned about the vulnerabilities inherent in these systems. From cleverly crafted prompts that can bypass safety measures to potential data leaks exposing sensitive information, the threats posed by GenAI are numerous and increasingly concerning. Elia Zaitsev, Chief Technology Officer of cybersecurity firm CrowdStrike, recently highlighted these issues in an interview with ZDNET. 

"This is a new attack vector that opens up a new attack surface," Zaitsev stated. He emphasized the hurried adoption of GenAI technologies, often at the expense of established security protocols. "I see with generative AI a lot of people just rushing to use this technology, and they're bypassing the normal controls and methods of secure computing," he explained. 

Zaitsev draws a parallel between GenAI and fundamental computing innovations. "In many ways, you can think of generative AI technology as a new operating system or a new programming language," he noted. The lack of widespread expertise in handling the pros and cons of GenAI compounds the problem, making it challenging to use and secure these systems effectively. The risk extends beyond poorly designed applications. 

According to Zaitsev, the centralization of valuable information within large language models (LLMs) presents a significant vulnerability. "The same problem of centralizing a bunch of valuable information exists with all LLM technology," he said. 

To mitigate these risks, Zaitsev advises against allowing LLMs unfettered access to data stores. Instead, he recommends a more controlled approach. "In a sense, you must tame RAG before it makes the problem worse," he suggested. This involves leveraging the LLM's capability to interpret open-ended questions and using traditional programming methods to fulfill queries securely. "For example, Charlotte AI often lets users ask generic questions," Zaitsev explained. 

"What Charlotte does is identify the relevant part of the platform and the specific data set that holds the source of truth, then pulls from that via an API call, rather than allowing the LLM to query the database directly." 

As enterprises increasingly integrate GenAI into their operations, understanding and addressing its security implications is crucial. By implementing stringent control measures and fostering a deeper understanding of this technology, organizations can harness its potential while safeguarding their valuable data.
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Technology
AI AI in Cybersecurrity Cybersecurity Jobs GenAI ML Technology

The Future of Cybersecurity Jobs in an AI-Driven World

 

The Future of Cybersecurity Jobs in an AI-Driven World Artificial Intelligence (AI) is revolutionizing the cybersecurity landscape, enhancing both the capabilities of cyber attackers and defenders. But a pressing question remains: Will AI replace cybersecurity jobs in the future? AI is sparking debates in the cybersecurity community. Is it safe? Does it benefit the good guys or the bad guys more? And crucially, how will it impact jobs in the industry? 

Here, we explore what modern AI is, its role in cybersecurity, and its potential effects on your career. Let’s delve into it. 

What is Modern AI? 

Modern AI involves building computer systems that can do tasks usually needing human intelligence. It uses algorithms and trains Large Language Models (LLMs) with lots of data to make accurate decisions. These models connect related topics through artificial neural networks, improving their decision-making through continuous data training. This process is called machine learning or deep learning. AI can now handle tasks like recognizing images, processing language, and learning from feedback in robotics and video games. AI tools are now integrated with complex systems to automate data analysis. This trend began with ChatGPT and has expanded to include AI image generation tools like MidJourney and domain-specific tools like GitHub Copilot. 

Despite their impressive capabilities, AI has limitations. AI in Cybersecurity AI is playing a big role in cybersecurity. Here are some key insights from a report called "Turning the Tide," based on interviews with 500 IT leaders: 

Job Security Concerns: Only 9% of respondents are confident AI will not replace their jobs in the next decade. Nearly one-third think AI will automate all cybersecurity tasks eventually. 

AI-Enhanced Attacks: Nearly 20% of respondents expect attackers to use AI to improve their strategies by 2025. 

Future Predictions: By 2030, a quarter of IT leaders believe data access will depend on biometric or DNA data, making unauthorized access impossible. Other predictions include less investment in physical property due to remote work, 5G transforming network security, and AI-automated security systems. 

"AI is a useful tool in defending against threats, but its value can only be harnessed with human expertise”, Bharat Mistry from Trend Micro reported. 

AI's Limitations in Cybersecurity 

Despite its potential, AI has several limitations requiring human oversight: 

Lack of Contextual Understanding: AI can analyze large data sets but can't grasp the psychological aspects of cyber defense, like hacker motivations. Human intervention is crucial for complex threats needing deep context. 

Inaccurate Results: AI tools can generate false positives and negatives, wasting resources or missing threats. Humans need to review AI alerts to ensure critical threats are addressed. 

Adversarial Attacks: As AI use grows, attacks against AI models, such as poisoning malware scanners to misidentify threats, will likely increase. Human oversight is essential to counter these manipulations. 

AI Bias: AI systems trained on biased data can produce biased results, affecting cybersecurity. Human oversight is necessary to mitigate biases and ensure accurate defenses. 


As AI evolves, cybersecurity professionals must adapt by continuously learning about AI advancements and their impact on security, developing AI and machine learning skills, enhancing critical thinking and contextual understanding, and collaborating with AI as a tool to augment their capabilities. Effective human-AI collaboration will be crucial for future cybersecurity strategies.
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Technology
Artifcial Intelligence Cognizant GenAI IT Sector LLMs Technology

IT and Consulting Firms Leverage Generative AI for Employee Development


Generative AI (GenAI) has emerged as a driving focus area in the learning and development (L&D) strategies of IT and consulting firms. Companies are increasingly investing in comprehensive training programs to equip their employees with essential GenAI skills, spanning from basic concepts to advanced technical know-how.

Training courses in GenAI cover a wide range of topics. Introductory courses, which can be completed in just a few hours, address the fundamentals, ethics, and social implications of GenAI. For those seeking deeper knowledge, advanced modules are available that focus on development using GenAI and large language models (LLMs), requiring over 100 hours to complete.

These courses are designed to cater to various job roles and functions within the organisations. For example, KPMG India aims to have its entire workforce trained in GenAI by the end of the fiscal year, with 50% already trained. Their programs are tailored to different levels of employees, from teaching leaders about return on investment and business envisioning to training coders in prompt engineering and LLM operations.

EY India has implemented a structured approach, offering distinct sets of courses for non-technologists, software professionals, project managers, and executives. Presently, 80% of their employees are trained in GenAI. Similarly, PwC India focuses on providing industry-specific masterclasses for leaders to enhance their client interactions, alongside offering brief nano courses for those interested in the basics of GenAI.

Wipro organises its courses into three levels based on employee seniority, with plans to develop industry-specific courses for domain experts. Cognizant has created shorter courses for leaders, sales, and HR teams to ensure a broad understanding of GenAI. Infosys also has a program for its senior leaders, with 400 of them currently enrolled.

Ray Wang, principal analyst and founder at Constellation Research, highlighted the extensive range of programs developed by tech firms, including training on Python and chatbot interactions. Cognizant has partnerships with Udemy, Microsoft, Google Cloud, and AWS, while TCS collaborates with NVIDIA, IBM, and GitHub.

Cognizant boasts 160,000 GenAI-trained employees, and TCS offers a free GenAI course on Oracle Cloud Infrastructure until the end of July to encourage participation. According to TCS's annual report, over half of its workforce, amounting to 300,000 employees, have been trained in generative AI, with a goal of training all staff by 2025.

The investment in GenAI training by IT and consulting firms pivots towards the importance of staying ahead in the rapidly evolving technological landscape. By equipping their employees with essential AI skills, these companies aim to enhance their capabilities, drive innovation, and maintain a competitive edge in the market. As the demand for AI expertise grows, these training programs will play a crucial role in shaping the future of the industry.


 

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zero-trust solutions
AI governance Cybersecurity Threats GenAI SaaS security Technology zero-trust solutions

GenAI Presents a Fresh Challenge for SaaS Security Teams

The software industry witnessed a pivotal moment with the introduction of Open AI's ChatGPT in November 2022, sparking a race dubbed the GenAI race. This event spurred SaaS vendors into a frenzy to enhance their tools with generative AI-driven productivity features.

GenAI tools serve a multitude of purposes, simplifying software development for developers, aiding sales teams in crafting emails, assisting marketers in creating low-cost unique content, and facilitating brainstorming sessions for teams and creatives.

Notable recent launches in the GenAI space include Microsoft 365 Copilot, GitHub Copilot, and Salesforce Einstein GPT, all of which are paid enhancements, indicating the eagerness of SaaS providers to capitalize on the GenAI trend. Google is also gearing up to launch its SGE (Search Generative Experience) platform, offering premium AI-generated summaries instead of conventional website listings.

The rapid integration of AI capabilities into SaaS applications suggests that it won't be long before AI becomes a standard feature in such tools.

However, alongside these advancements come new risks and challenges for users. The widespread adoption of GenAI applications in workplaces is raising concerns about exposure to cybersecurity threats.

GenAI operates by training models to generate data similar to the original based on user-provided information. This exposes organizations to risks such as IP leakage, exposure of sensitive customer data, and the potential for cybercriminals to use deepfakes for phishing scams and identity theft.

These concerns, coupled with the need to comply with regulations, have led to a backlash against GenAI applications, especially in industries handling confidential data. Some organizations have even banned the use of GenAI tools altogether.

Despite these bans, organizations struggle to control the use of GenAI applications effectively, as they often enter the workplace without proper oversight or approval.

In response to these challenges, the US government is urging organizations to implement better governance around AI usage. This includes appointing Chief AI Officers to oversee AI technologies and ensure responsible usage.

With the rise of GenAI applications, organizations need to reassess their security measures. Traditional perimeter protection strategies are proving inadequate against modern threats, which target vulnerabilities within organizations.

To regain control and mitigate risks associated with GenAI apps, organizations can adopt advanced zero-trust solutions like SSPM (SaaS Security Posture Management). These solutions provide visibility into AI-enabled apps and assess their security posture to prevent, detect, and respond to threats effectively.

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