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IT Security
AI Agent AI Chatbots AI cybersecurity AI technology Cyber Security cybersecurity concerns IT Sector IT Security

AI Agents Raise Cybersecurity Concerns Amid Rapid Enterprise Adoption

 

A growing number of organizations are adopting autonomous AI agents despite widespread concerns about the cybersecurity risks they pose. According to a new global report released by identity security firm SailPoint, this accelerated deployment is happening in a largely unregulated environment. The findings are based on a survey of more than 350 IT professionals, revealing that 84% of respondents said their organizations already use AI agents internally. 

However, only 44% confirmed the presence of any formal policies to regulate the agents’ actions. AI agents differ from traditional chatbots in that they are designed to independently plan and execute tasks without constant human direction. Since the emergence of generative AI tools like ChatGPT in late 2022, major tech companies have been racing to launch their own agents. Many smaller businesses have followed suit, motivated by the desire for operational efficiency and the pressure to adopt what is widely viewed as a transformative technology.  

Despite this enthusiasm, 96% of survey participants acknowledged that these autonomous systems pose security risks, while 98% stated their organizations plan to expand AI agent usage within the next year. The report warns that these agents often have extensive access to sensitive systems and information, making them a new and significant attack surface for cyber threats. Chandra Gnanasambandam, SailPoint’s Executive Vice President of Product and Chief Technology Officer, emphasized the risks associated with such broad access. He explained that these systems are transforming workflows but typically operate with minimal oversight, which introduces serious vulnerabilities. 

Further compounding the issue is the inconsistent implementation of governance controls. Although 92% of those surveyed agree that AI agents should be governed similarly to human employees, 80% reported incidents where agents performed unauthorized actions or accessed restricted data. These incidents underscore the dangers of deploying autonomous systems without robust monitoring or access controls. 

Gnanasambandam suggests adopting an identity-first approach to agent management. He recommends applying the same security protocols used for human users, including real-time access permissions, least privilege principles, and comprehensive activity tracking. Without such measures, organizations risk exposing themselves to breaches or data misuse due to the very tools designed to streamline operations. 

As AI agents become more deeply embedded in business processes, experts caution that failing to implement adequate oversight could create long-term vulnerabilities. The report serves as a timely reminder that innovation must be accompanied by strong governance to ensure cybersecurity is not compromised in the pursuit of automation.
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Robotics
AI technology AI tools Cyber Security Generative AI generative AI tools Job Automation Job Security Llama 3 Llama 4 manufacturing industry Robotics

Foxconn’s Chairman Warns AI and Robotics Will Replace Low-End Manufacturing Jobs

 

Foxconn chairman Young Liu has issued a stark warning about the future of low-end manufacturing jobs, suggesting that generative AI and robotics will eventually eliminate many of these roles. Speaking at the Computex conference in Taiwan, Liu emphasized that this transformation is not just technological but geopolitical, urging world leaders to prepare for the sweeping changes ahead. 

According to Liu, wealthy nations have historically relied on two methods to keep manufacturing costs down: encouraging immigration to bring in lower-wage workers and outsourcing production to countries with lower GDP. However, he argued that both strategies are reaching their limits. With fewer low-GDP countries to outsource to and increasing resistance to immigration in many parts of the world, Liu believes that generative AI and robotics will be the next major solution to bridge this gap. He cited Foxconn’s own experience as proof of this shift. 

After integrating generative AI into its production processes, the company discovered that AI alone could handle up to 80% of the work involved in setting up new manufacturing runs—often faster than human workers. While human input is still required to complete the job, the combination of AI and skilled labor significantly improves efficiency. As a result, Foxconn’s human experts are now able to focus on more complex challenges rather than repetitive tasks. Liu also announced the development of a proprietary AI model named “FoxBrain,” tailored specifically for manufacturing. 

Built using Meta’s Llama 3 and 4 models and trained on Foxconn’s internal data, this tool aims to automate workflows and enhance factory operations. The company plans to open-source FoxBrain and deploy it across all its facilities, continuously improving the model with real-time performance feedback. Another innovation Liu highlighted was Foxconn’s use of Nvidia’s Omniverse to create digital twins of future factories. These AI-operated virtual factories are used to test and optimize layouts before construction begins, drastically improving design efficiency and effectiveness. 

In addition to manufacturing, Foxconn is eyeing the electric vehicle sector. Liu revealed the company is working on a reference design for EVs, a model that partners can customize—much like Foxconn’s strategy with PC manufacturers. He claimed this approach could reduce product development workloads by up to 80%, enhancing time-to-market and cutting costs. 

Liu closed his keynote by encouraging industry leaders to monitor these developments closely, as the rise of AI-driven automation could reshape the global labor landscape faster than anticipated.
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Privacy
AI Systems AI technology AI tools customer data privacy Data Privacy Data Safety User Data data security Data Usage Google Google AI model Privacy

Google’s AI Virtual Try-On Tool Redefines Online Shopping Experience

 

At the latest Google I/O developers conference, the tech giant introduced an unexpected innovation in online shopping: an AI-powered virtual try-on tool. This new feature lets users upload a photo of themselves and see how clothing items would appear on their body. By merging the image of the user with that of the garment, Google’s custom-built image generation model creates a realistic simulation of the outfit on the individual. 

While the concept seems simple, the underlying AI technology is advanced. In a live demonstration, the tool appeared to function seamlessly. The feature is now available in the United States and is part of Google’s broader efforts to enhance the online shopping experience through AI integration. It’s particularly useful for people who often struggle to visualize how clothing will look on their body compared to how it appears on models.  

However, the rollout of this tool raised valid questions about user privacy. AI systems that involve personal images often come with concerns over data usage. Addressing these worries, a Google representative clarified that uploaded photos are used exclusively for the try-on experience. The images are not stored for AI training, are not shared with other services or third parties, and users can delete or update their photos at any time. This level of privacy protection is notable in an industry where user data is typically leveraged to improve algorithms. 

Given Google’s ongoing development of AI-driven tools, some expected the company to utilize this photo data for model training. Instead, the commitment to user privacy in this case suggests a more responsible approach. Virtual fitting technology isn’t entirely new. Retail and tech companies have been exploring similar ideas for years. Amazon, for instance, has experimented with AI tools in its fashion division. Google, however, claims its new tool offers a more in-depth understanding of diverse body types. 

During the presentation, Vidhya Srinivasan, Google’s VP of ads and commerce, emphasized the system’s goal of accommodating different shapes and sizes more effectively. Past AI image tools have faced criticism for lacking diversity and realism. It’s unclear whether Google’s new tool will be more reliable across the board. Nevertheless, their assurance that user images won’t be used to train models helps build trust. 

Although the virtual preview may not always perfectly reflect real-life appearances, this development points to a promising direction for AI in retail. If successful, it could improve customer satisfaction, reduce returns, and make online shopping a more personalized experience.
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OpenAI partnership
AI capabilities AI Systems AI technology AI Tool Customer Service Cyber Security Fintech OpenAI OpenAI partnership

Klarna Scales Back AI-Led Customer Service Strategy, Resumes Human Support Hiring

 

Klarna Group Plc, the Sweden-based fintech company, is reassessing its heavy reliance on artificial intelligence (AI) in customer service after admitting the approach led to a decline in service quality. CEO and co-founder Sebastian Siemiatkowski acknowledged that cost-cutting took precedence over customer experience during a company-wide AI push that replaced hundreds of human agents. 

Speaking at Klarna’s Stockholm headquarters, Siemiatkowski conceded, “As cost unfortunately seems to have been a too predominant evaluation factor when organizing this, what you end up having is lower quality.” The company had frozen hiring for over a year to scale its AI capabilities but now plans to recalibrate its customer service model. 

In a strategic shift, Klarna is restarting recruitment for customer support roles — a rare move that reflects the company’s need to restore the quality of human interaction. A new pilot program is underway that allows remote workers — including students and individuals in rural areas — to provide customer service on-demand in an “Uber-like setup.” Currently, two agents are part of the trial. “We also know there are tons of Klarna users that are very passionate about our company and would enjoy working for us,” Siemiatkowski said. 

He stressed the importance of giving customers the option to speak to a human, citing both brand and operational needs. Despite dialing back on AI-led customer support, Klarna is not walking away from AI altogether. The company is continuing to rebuild its tech stack with AI at the core, aiming to improve operational efficiency. It is also developing a digital financial assistant designed to help users secure better interest rates and insurance options. 

Klarna maintains a close relationship with OpenAI, a collaboration that began in 2023. “We wanted to be [OpenAI’s] favorite guinea pig,” Siemiatkowski noted, reinforcing the company’s long-term commitment to leveraging AI. Klarna’s course correction follows a turbulent financial period. After peaking at a $45.6 billion valuation in 2021, the company saw its value drop to $6.7 billion in 2022. It has since rebounded and aims to raise $1 billion via an IPO, targeting a valuation exceeding $15 billion — though IPO plans have been paused due to market volatility. 

The company’s 2024 announcement that AI was handling the workload of 700 human agents disrupted the call center industry, leading to a sharp drop in shares of Teleperformance SE, a major outsourcing firm. While Klarna is resuming hiring, its overall workforce is expected to shrink. “In a year’s time, we’ll probably be down to about 2,500 people from 3,000,” Siemiatkowski said, noting that attrition and further AI improvements will likely drive continued headcount reductions.
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Quantum computing security
AI technology AI tools Cyber Security financial services Generative AI Machine learning PQC Quantum computing Quantum computing security

Quantum Computing Could Deliver Business Value by 2028 with 100 Logical Qubits

 

Quantum computing may soon move from theory to commercial reality, as experts predict that machines with 100 logical qubits could start delivering tangible business value by 2028—particularly in areas like material science. Speaking at the Commercialising Quantum Computing conference in London, industry leaders suggested that such systems could outperform even high-performance computing in solving complex problems. 

Mark Jackson, senior quantum evangelist at Quantinuum, highlighted that quantum computing shows great promise in generative AI applications, especially machine learning. Unlike traditional systems that aim for precise answers, quantum computers excel at identifying patterns in large datasets—making them highly effective for cybersecurity and fraud detection. “Quantum computers can detect patterns that would be missed by other conventional computing methods,” Jackson said.  

Financial services firms are also beginning to realize the potential of quantum computing. Phil Intallura, global head of quantum technologies at HSBC, said quantum technologies can help create more optimized financial models. “If you can show a solution using quantum technology that outperforms supercomputers, decision-makers are more likely to invest,” he noted. HSBC is already exploring quantum random number generation for use in simulations and risk modeling. 

In a recent collaborative study published in Nature, researchers from JPMorgan Chase, Quantinuum, Argonne and Oak Ridge national labs, and the University of Texas showcased Random Circuit Sampling (RCS) as a certified-randomness-expansion method, a task only achievable on a quantum computer. This work underscores how randomness from quantum systems can enhance classical financial simulations. Quantum cryptography also featured prominently at the conference. Regulatory pressure is mounting on banks to replace RSA-2048 encryption with quantum-safe standards by 2035, following recommendations from the U.S. National Institute of Standards and Technology. 

Santander’s Mark Carney emphasized the need for both software and hardware support to enable fast and secure post-quantum cryptography (PQC) in customer-facing applications. Gerard Mullery, interim CEO at Oxford Quantum Circuits, stressed the importance of integrating quantum computing into traditional enterprise workflows. As AI increasingly automates business processes, quantum platforms will need to support seamless orchestration within these ecosystems. 

While only a few companies have quantum machines with logical qubits today, the pace of development suggests that quantum computing could be transformative within the next few years. With increasing investment and maturing use cases, businesses are being urged to prepare for a hybrid future where classical and quantum systems work together to solve previously intractable problems.
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personal data security
AI Deepfakes AI technology AI tools Child Identity Theft Cyber Security Deepfake Deepfake Images deepfake videos Identity Theft personal data security

AI Can Create Deepfake Videos of Children Using Just 20 Images, Expert Warns

 

Parents are being urged to rethink how much they share about their children online, as experts warn that criminals can now generate realistic deepfake videos using as few as 20 images. This alarming development highlights the growing risks of digital identity theft and fraud facing children due to oversharing on social media platforms.  

According to Professor Carsten Maple of the University of Warwick and the Alan Turing Institute, modern AI tools can construct highly realistic digital profiles, including 30-second deepfake videos, from a small number of publicly available photos. These images can be used not only by criminal networks to commit identity theft, open fraudulent accounts, or claim government benefits in a child’s name but also by large tech companies to train their algorithms, often without the user’s full awareness or consent. 

New research conducted by Perspectus Global and commissioned by Proton surveyed 2,000 UK parents of children under 16. The findings show that on average, parents upload 63 images to social media every month, with 59% of those being family-related. A significant proportion of parents—21%—share these photos multiple times a week, while 38% post several times a month. These frequent posts not only showcase images but also often contain sensitive data like location tags and key life events, making it easier for bad actors to build a detailed online profile of the child. Professor Maple warned that such oversharing can lead to long-term consequences. 

Aside from potential identity theft, children could face mental distress or reputational harm later in life from having a permanent digital footprint that they never consented to create. The problem is exacerbated by the fact that many parents are unaware of how their data is being used. For instance, 48% of survey respondents did not realize that cloud storage providers can access the data stored on their platforms. In fact, more than half of the surveyed parents (56%) store family images on cloud services such as Google Drive or Apple iCloud. On average, each parent had 185 photos of their children stored digitally—images that may be accessed or analyzed under vaguely worded terms and conditions.  

Recent changes to Instagram’s user agreement, which now allows the platform to use uploaded images to train its AI systems, have further heightened privacy concerns. Additionally, experts have warned about the use of personal images by other Big Tech firms to enhance facial recognition algorithms and advertising models. To protect their children, parents are advised to implement a range of safety measures. These include using secure and private cloud storage, adjusting privacy settings on social platforms, avoiding public Wi-Fi when sharing or uploading data, and staying vigilant against phishing scams. 

Furthermore, experts recommend setting boundaries with children regarding online activity, using parental controls, antivirus tools, and search filters, and modeling responsible digital behavior. The growing accessibility of AI-based image manipulation tools underscores the urgent need for greater awareness and proactive digital hygiene. What may seem like harmless sharing today could expose children to significant risks in the future.
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Network Security
AI cybersecurity AI cybersecurity tools AI technology AI tools Cloud Security Cyber Defenses Cyber Security Endpoint IAM Machine learning Network Security

AI in Cybersecurity Market Sees Rapid Growth as Network Security Leads 2024 Expansion

 

The integration of artificial intelligence into cybersecurity solutions has accelerated dramatically, driving the global market to an estimated value of $32.5 billion in 2024. This surge—an annual growth rate of 23%—reflects organizations’ urgent need to defend against increasingly sophisticated cyber threats. Traditional, signature-based defenses are no longer sufficient; today’s adversaries employ polymorphic malware, fileless attacks, and automated intrusion tools that can evade static rule sets. AI’s ability to learn patterns, detect anomalies in real time, and respond autonomously has become indispensable. 

Among AI-driven cybersecurity segments, network security saw the most significant expansion last year, accounting for nearly 40% of total AI security revenues. AI-enhanced intrusion prevention systems and next-generation firewalls leverage machine learning models to inspect vast streams of traffic, distinguishing malicious behavior from legitimate activity. These solutions can automatically quarantine suspicious connections, adapt to novel malware variants, and provide security teams with prioritized alerts—reducing mean time to detection from days to mere minutes. As more enterprises adopt zero-trust architectures, AI’s role in continuously verifying device and user behavior on the network has become a cornerstone of modern defensive strategies. 

Endpoint security followed closely, representing roughly 25% of the AI cybersecurity market in 2024. AI-powered endpoint detection and response (EDR) platforms monitor processes, memory activity, and system calls on workstations and servers. By correlating telemetry across thousands of devices, these platforms can identify subtle indicators of compromise—such as unusual parent‑child process relationships or command‑line flags—before attackers achieve persistence. The rise of remote work has only heightened demand: with employees connecting from diverse locations and personal devices, AI’s context-aware threat hunting capabilities help maintain comprehensive visibility across decentralized environments. 

Identity and access management (IAM) solutions incorporating AI now capture about 20% of the market. Behavioral analytics engines analyze login patterns, device characteristics, and geolocation data to detect risky authentication attempts. Rather than relying solely on static multi‑factor prompts, adaptive authentication methods adjust challenge levels based on real‑time risk scores, blocking illicit logins while minimizing friction for legitimate users. This dynamic approach addresses credential stuffing and account takeover attacks, which accounted for over 30% of cyber incidents in 2024. Cloud security, covering roughly 15% of the AI cybersecurity spend, is another high‑growth area. 

With workloads distributed across public, private, and hybrid clouds, AI-driven cloud security posture management (CSPM) tools continuously scan configurations and user activities for misconfigurations, vulnerable APIs, and data‑exfiltration attempts. Automated remediation workflows can instantly correct risky settings, enforce encryption policies, and isolate compromised workloads—ensuring compliance with evolving regulations such as GDPR and CCPA. 

Looking ahead, analysts predict the AI in cybersecurity market will exceed $60 billion by 2028, as vendors integrate generative AI for automated playbook creation and incident response orchestration. Organizations that invest in AI‑powered defenses will gain a competitive edge, enabling proactive threat hunting and resilient operations against a backdrop of escalating cyber‑threat complexity.
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RSA Conference
agentic AI AI cybersecurity tools AI Systems AI technology Career Cyber Security cybersecurity landscape RSA RSA Conference

Agentic AI Is Reshaping Cybersecurity Careers, Not Replacing Them

 

Agentic AI took center stage at the 2025 RSA Conference, signaling a major shift in how cybersecurity professionals will work in the near future. No longer a futuristic concept, agentic AI systems—capable of planning, acting, and learning independently—are already being deployed to streamline incident response, bolster compliance, and scale threat detection efforts. These intelligent agents operate with minimal human input, making real-time decisions and adapting to dynamic environments. 

While the promise of increased efficiency and resilience is driving rapid adoption, cybersecurity leaders also raised serious concerns. Experts like Elastic CISO Mandy Andress called for greater transparency and stronger oversight when deploying AI agents in sensitive environments. Trust, explainability, and governance emerged as recurring themes throughout RSAC, underscoring the need to balance innovation with caution—especially as cybercriminals are also experimenting with agentic AI to enhance and scale their attacks. 

For professionals in the field, this isn’t a moment to fear job loss—it’s a chance to embrace career transformation. New roles are already emerging. AI-Augmented Cybersecurity Analysts will shift from routine alert triage to validating agent insights and making strategic decisions. Security Agent Designers will define logic workflows and trust boundaries for AI operations, blending DevSecOps with AI governance. Meanwhile, AI Threat Hunters will work to identify how attackers may exploit these new tools and develop defense mechanisms in response. 

Another critical role on the horizon is the Autonomous SOC Architect, tasked with designing next-generation security operations centers powered by human-machine collaboration. There will also be growing demand for Governance and AI Ethics Leads who ensure that decisions made by AI agents are auditable, compliant, and ethically sound. These roles reflect how cybersecurity is evolving into a hybrid discipline requiring both technical fluency and ethical oversight. 

To stay competitive in this changing landscape, professionals should build new skills. This includes prompt engineering, agent orchestration using tools like LangChain, AI risk modeling, secure deployment practices, and frameworks for explainability. Human-AI collaboration strategies will also be essential, as security teams learn to partner with autonomous systems rather than merely supervise them. As IBM’s Suja Viswesan emphasized, “Security must be baked in—not bolted on.” That principle applies not only to how organizations deploy agentic AI but also to how they train and upskill their cybersecurity workforce. 

The future of defense depends on professionals who understand how AI agents think, operate, and fail. Ultimately, agentic AI isn’t replacing people—it’s reshaping their roles. Human intuition, ethical reasoning, and strategic thinking remain vital in defending against modern cyber threats. 

As HackerOne CEO Kara Sprague noted, “Machines detect patterns. Humans understand motives.” Together, they can form a faster, smarter, and more adaptive line of defense. The cybersecurity industry isn’t just gaining new tools—it’s creating entirely new job titles and disciplines.
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Ransomwares
agentic AI AI Agent AI technology AI threats AI tools Cyber Security Cybersecurity Threats IABs Malicious Code Ransomwares

Agentic AI and Ransomware: How Autonomous Agents Are Reshaping Cybersecurity Threats

 

A new generation of artificial intelligence—known as agentic AI—is emerging, and it promises to fundamentally change how technology is used. Unlike generative AI, which mainly responds to prompts, agentic AI operates independently, solving complex problems and making decisions without direct human input. While this leap in autonomy brings major benefits for businesses, it also introduces serious risks, especially in the realm of cybersecurity. Security experts warn that agentic AI could significantly enhance the capabilities of ransomware groups. 

These autonomous agents can analyze, plan, and execute tasks on their own, making them ideal tools for attackers seeking to automate and scale their operations. As agentic AI evolves, it is poised to alter the cyber threat landscape, potentially enabling more efficient and harder-to-detect ransomware attacks. In contrast to the early concerns raised in 2022 with the launch of tools like ChatGPT, which mainly helped attackers draft phishing emails or debug malicious code, agentic AI can operate in real time and adapt to complex environments. This allows cybercriminals to offload traditionally manual processes like lateral movement, system enumeration, and target prioritization. 

Currently, ransomware operators often rely on Initial Access Brokers (IABs) to breach networks, then spend time manually navigating internal systems to deploy malware. This process is labor-intensive and prone to error, often leading to incomplete or failed attacks. Agentic AI, however, removes many of these limitations. It can independently identify valuable targets, choose the most effective attack vectors, and adjust to obstacles—all without human direction. These agents may also dramatically reduce the time required to carry out a successful ransomware campaign, compressing what once took weeks into mere minutes. 

In practice, agentic AI can discover weak points in a network, bypass defenses, deploy malware, and erase evidence of the intrusion—all in a single automated workflow. However, just as agentic AI poses a new challenge for cybersecurity, it also offers potential defensive benefits. Security teams could deploy autonomous AI agents to monitor networks, detect anomalies, or even create decoy systems that mislead attackers. 

While agentic AI is not yet widely deployed by threat actors, its rapid development signals an urgent need for organizations to prepare. To stay ahead, companies should begin exploring how agentic AI can be integrated into their defense strategies. Being proactive now could mean the difference between falling behind or successfully countering the next wave of ransomware threats.
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SBI
AI Scams AI technology Cyber Security Deepfake deepfake scams deepfake videos Investment Scam SBI

SBI Issues Urgent Warning Against Deepfake Scam Videos Promoting Fake Investment Schemes

 

The State Bank of India (SBI) has issued an urgent public advisory warning customers and the general public about the rising threat of deepfake scam videos. These videos, circulating widely on social media, falsely claim that SBI has launched an AI-powered investment scheme in collaboration with the Government of India and multinational corporations—offering unusually high returns. 

SBI categorically denied any association with such platforms and urged individuals to verify investment-related information through its official website, social media handles, or local branches. The bank emphasized that it does not endorse or support any investment services that promise guaranteed or unrealistic returns. 

In a statement published on its official X (formerly Twitter) account, SBI stated: “State Bank of India cautions all its customers and the general public about many deepfake videos being circulated on social media, falsely claiming the launch of an AI-based platform showcasing lucrative investment schemes supported by SBI in association with the Government of India and some multinational companies. These videos misuse technology to create false narratives and deceive people into making financial commitments in fraudulent schemes. We clarify that SBI does not endorse any such schemes that promise unrealistic or unusually high returns.” 

Deepfake technology, which uses AI to fabricate convincing videos by manipulating facial expressions and voices, has increasingly been used to impersonate public figures and create fake endorsements. These videos often feature what appear to be real speeches or statements by senior officials or celebrities, misleading viewers into believing in illegitimate financial products. This isn’t an isolated incident. Earlier this year, a deepfake video showing India’s Union Finance Minister allegedly promoting an investment platform was debunked by the government’s PIB Fact Check. 

The video, which falsely claimed that viewers could earn a steady daily income through the platform, was confirmed to be digitally altered. SBI’s warning is part of a broader effort to combat the misuse of emerging technologies for financial fraud. The bank is urging everyone to remain cautious and to avoid falling prey to such digital deceptions. Scammers are increasingly using advanced AI tools to exploit public trust and create a false sense of legitimacy. 

To protect themselves, customers are advised to verify any financial information or offers through official SBI channels. Any suspicious activity or misleading promotional material should be reported immediately. SBI’s proactive communication reinforces its commitment to safeguarding customers in an era where financial scams are becoming more sophisticated. The bank’s message is clear: do not trust any claims about investment opportunities unless they come directly from verified, official sources.
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Silicon Valley
AI generated Deepfake AI Security AI technology AI Tool Breach attack Cyber Security Hacker attack Silicon Valley

Silicon Valley Crosswalk Buttons Hacked With AI Voices Mimicking Tech Billionaires

 

A strange tech prank unfolded across Silicon Valley this past weekend after crosswalk buttons in several cities began playing AI-generated voice messages impersonating Elon Musk and Mark Zuckerberg.  

Pedestrians reported hearing bizarre and oddly personal phrases coming from audio-enabled crosswalk systems in Menlo Park, Palo Alto, and Redwood City. The altered voices were crafted to sound like the two tech moguls, with messages that ranged from humorous to unsettling. One button, using a voice resembling Zuckerberg, declared: “We’re putting AI into every corner of your life, and you can’t stop it.” Another, mimicking Musk, joked about loneliness and buying a Cybertruck to fill the void.  

The origins of the incident remain unknown, but online speculation points to possible hacktivism—potentially aimed at critiquing Silicon Valley’s AI dominance or simply poking fun at tech culture. Videos of the voice spoof spread quickly on TikTok and X, with users commenting on the surreal experience and sarcastically suggesting the crosswalks had been “venture funded.” This situation prompts serious concern. 

Local officials confirmed they’re investigating the breach and working to restore normal functionality. According to early reports, the tampering may have taken place on Friday. These crosswalk buttons aren’t new—they’re part of accessibility technology designed to help visually impaired pedestrians cross streets safely by playing audio cues. But this incident highlights how vulnerable public infrastructure can be to digital interference. Security researchers have warned in the past that these systems, often managed with default settings and unsecured firmware, can be compromised if not properly protected. 

One expert, physical penetration specialist Deviant Ollam, has previously demonstrated how such buttons can be manipulated using unchanged passwords or open ports. Polara, a leading manufacturer of these audio-enabled buttons, did not respond to requests for comment. The silence leaves open questions about how widespread the vulnerability might be and what cybersecurity measures, if any, are in place. This AI voice hack not only exposed weaknesses in public technology but also raised broader questions about the blending of artificial intelligence, infrastructure, and data privacy. 

What began as a strange and comedic moment at the crosswalk is now fueling a much larger conversation about the cybersecurity risks of increasingly connected cities. With AI becoming more embedded in daily life, events like this might be just the beginning of new kinds of public tech disruptions.
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Misinformation
AI Fraud AI Platform AI Scams AI technology AI tools Cyber Security data security Fake Documents Generative AI Identity Theft Misinformation

Generative AI Fuels Identity Theft, Aadhaar Card Fraud, and Misinformation in India

 

A disturbing trend is emerging in India’s digital landscape as generative AI tools are increasingly misused to forge identities and spread misinformation. One user, Piku, revealed that an AI platform generated a convincing Aadhaar card using only a name, birth date, and address—raising serious questions about data security. While AI models typically do not use real personal data, the near-perfect replication of government documents hints at training on real-world samples, possibly sourced from public leaks or open repositories. 

This AI-enabled fraud isn’t occurring in isolation. Criminals are combining fake document templates with authentic data collected from discarded paperwork, e-waste, and old printers. The resulting forged identities are realistic enough to pass basic checks, enabling SIM card fraud, bank scams, and more. What started as tools for entertainment and productivity now pose serious risks. Misinformation tactics are evolving too. 

A recent incident involving playback singer Shreya Ghoshal illustrated how scammers exploit public figures to push phishing links. These fake stories led users to malicious domains targeting them with investment scams under false brand names like Lovarionix Liquidity. Cyber intelligence experts traced these campaigns to websites built specifically for impersonation and data theft. The misuse of generative AI also extends into healthcare fraud. 

In a shocking case, a man impersonated renowned cardiologist Dr. N John Camm and performed unauthorized surgeries at a hospital in Madhya Pradesh. At least two patient deaths were confirmed between December 2024 and February 2025. Investigators believe the impersonator may have used manipulated or AI-generated credentials to gain credibility. Cybersecurity professionals are urging more vigilance. CertiK founder Ronghui Gu emphasizes that users must understand the risks of sharing biometric data, like facial images, with AI platforms. Without transparency, users cannot be sure how their data is used or whether it’s shared. He advises precautions such as using pseudonyms, secondary emails, and reading privacy policies carefully—especially on platforms not clearly compliant with regulations like GDPR or CCPA. 

A recent HiddenLayer report revealed that 77% of companies using AI have already suffered security breaches. This underscores the need for robust data protection as AI becomes more embedded in everyday processes. India now finds itself at the center of an escalating cybercrime wave powered by generative AI. What once seemed like harmless innovation now fuels identity theft, document forgery, and digital misinformation. The time for proactive regulation, corporate accountability, and public awareness is now—before this new age of AI-driven fraud becomes unmanageable.
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HR Department
Ai Data AI technology AI tools Cyber Security Data Services Data Technology GenAI HR Department

How GenAI Is Revolutionizing HR Analytics for CHROs and Business Leaders

 

Generative AI (GenAI) is redefining how HR leaders interact with data, removing the steep learning curve traditionally associated with people analytics tools. When faced with a spike in hourly employee turnover, Sameer Raut, Vice President of HRIS at Sunstate Equipment, didn’t need to build a custom report or consult data scientists. Instead, he typed a plain-language query into a GenAI-powered chatbot: 

“What are the top reasons for hourly employee terminations in the past 12 months?” Within seconds, he had his answer. This shift in how HR professionals access data marks a significant evolution in workforce analytics. Tools powered by large language models (LLMs) are now integrated into leading analytics platforms such as Visier, Microsoft Power BI, Tableau, Qlik, and Sisense. These platforms are leveraging GenAI to interpret natural language questions and deliver real-time, actionable insights without requiring technical expertise. 

One of the major advantages of GenAI is its ability to unify fragmented HR data sources. It streamlines data cleansing, ensures consistency, and improves the accuracy of workforce metrics like headcount growth, recruitment gaps, and attrition trends. As Raut notes, tools like Visier’s GenAI assistant “Vee” allow him to make quick decisions during meetings, helping HR become more responsive and strategic. This evolution is particularly valuable in a landscape where 39% of HR leaders cite limited analytics expertise as their biggest challenge, according to a 2023 Aptitude Research study. 

GenAI removes this barrier by enabling intuitive data exploration across familiar platforms like Slack and Microsoft Teams. Frontline managers who may never open a BI dashboard can now access performance metrics and workforce trends instantly. Experts believe this transformation is just beginning. While some analytics platforms are still improving their natural language processing capabilities, others are leading with more advanced and user-friendly GenAI chatbots. 

These tools can even create automated visualizations and summaries tailored to executive audiences, enabling CHROs to tell compelling data stories during high-level meetings. However, this transformation doesn’t come without risk. Data privacy remains a top concern, especially as GenAI tools engage with sensitive workforce data. HR leaders must ensure that platforms offer strict entitlement management and avoid training AI models on private customer data. Providers like Visier mitigate these risks by training their models solely on anonymized queries rather than real-world employee information. 

As GenAI continues to evolve, it’s clear that its role in HR will only expand. From democratizing access to HR data to enhancing real-time decision-making and storytelling, this technology is becoming indispensable for organizations looking to stay agile and informed.
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Technology
AI AI technology Airbnb LLM Technology

AI Powers Airbnb’s Code Migration, But Human Oversight Still Key, Say Tech Giants

 

In a bold demonstration of AI’s growing role in software development, Airbnb has successfully completed a large-scale code migration project using large language models (LLMs), dramatically reducing the timeline from an estimated 1.5 years to just six weeks. The project involved updating approximately 3,500 React component test files from Enzyme to the more modern React Testing Library (RTL). 

According to Airbnb software engineer Charles Covey-Brandt, the company’s AI-driven pipeline used a combination of automated validation steps and frontier LLMs to handle the bulk of the transformation. Impressively, 75% of the files were migrated within just four hours, thanks to robust automation and intelligent retries powered by dynamic prompt engineering with context-rich inputs of up to 100,000 tokens. 

Despite this efficiency, about 900 files initially failed validation. Airbnb employed iterative tools and a status-tracking system to bring that number down to fewer than 100, which were finally resolved manually—underscoring the continued need for human intervention in such processes. Other tech giants echo this hybrid approach. Google, in a recent report, noted a 50% speed increase in migrating codebases using LLMs. 

One project converting ID types in the Google Ads system—originally estimated to take hundreds of engineering years—was largely automated, with 80% of code changes authored by AI. However, inaccuracies still required manual edits, prompting Google to invest further in AI-powered verification. Amazon Web Services also highlighted the importance of human-AI collaboration in code migration. 

Its research into modernizing Java code using Amazon Q revealed that developers value control and remain cautious of AI outputs. Participants emphasized their role as reviewers, citing concerns about incorrect or misleading changes. While AI is accelerating what were once laborious coding tasks, these case studies reveal that full autonomy remains out of reach. 

Engineers continue to act as crucial gatekeepers, validating and refining AI-generated code. For now, the future of code migration lies in intelligent partnerships—where LLMs do the heavy lifting and humans ensure precision.
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encryption technology
AI Privacy AI Systems AI technology AI Tool Cryptography Cyber Security Data Privacy Deep Learning encryption technology

Orion Brings Fully Homomorphic Encryption to Deep Learning for AI Privacy

 

As data privacy becomes an increasing concern, a new artificial intelligence (AI) encryption breakthrough could transform how sensitive information is handled. Researchers Austin Ebel, Karthik Garimella, and Assistant Professor Brandon Reagen have developed Orion, a framework that integrates fully homomorphic encryption (FHE) into deep learning. 

This advancement allows AI systems to analyze encrypted data without decrypting it, ensuring privacy throughout the process. FHE has long been considered a major breakthrough in cryptography because it enables computations on encrypted information while keeping it secure. However, applying this method to deep learning has been challenging due to the heavy computational requirements and technical constraints. Orion addresses these challenges by automating the conversion of deep learning models into FHE-compatible formats. 

The researchers’ study, recently published on arXiv and set to be presented at the 2025 ACM International Conference on Architectural Support for Programming Languages and Operating Systems, highlights Orion’s ability to make privacy-focused AI more practical. One of the biggest concerns in AI today is that machine learning models require direct access to user data, raising serious privacy risks. Orion eliminates this issue by allowing AI to function without exposing sensitive information. The framework is built to work with PyTorch, a widely used machine learning library, making it easier for developers to integrate FHE into existing models. 

Orion also introduces optimization techniques that reduce computational burdens, making privacy-preserving AI more efficient and scalable. Orion has demonstrated notable performance improvements, achieving speeds 2.38 times faster than previous FHE deep learning methods. The researchers successfully implemented high-resolution object detection using the YOLO-v1 model, which contains 139 million parameters—a scale previously considered impractical for FHE. This progress suggests Orion could enable encrypted AI applications in sectors like healthcare, finance, and cybersecurity, where protecting user data is essential. 

A key advantage of Orion is its accessibility. Traditional FHE implementations require specialized knowledge, making them difficult to adopt. Orion simplifies the process, allowing more developers to use the technology without extensive training. By open-sourcing the framework, the research team hopes to encourage further innovation and adoption. As AI continues to expand into everyday life, advancements like Orion could help ensure that technological progress does not come at the cost of privacy and security.
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enterprise cybersecurity
AI Chatbots AI Risks AI technology Chatbots ChatGPT. OpenAI Cyber Security data security DeepSeek enterprise cybersecurity

DeepSeek AI: Benefits, Risks, and Security Concerns for Businesses

 

DeepSeek, an AI chatbot developed by China-based High-Flyer, has gained rapid popularity due to its affordability and advanced natural language processing capabilities. Marketed as a cost-effective alternative to OpenAI’s ChatGPT, DeepSeek has been widely adopted by businesses looking for AI-driven insights. 

However, cybersecurity experts have raised serious concerns over its potential security risks, warning that the platform may expose sensitive corporate data to unauthorized surveillance. Reports suggest that DeepSeek’s code contains embedded links to China Mobile’s CMPassport.com, a registry controlled by the Chinese government. This discovery has sparked fears that businesses using DeepSeek may unknowingly be transferring sensitive intellectual property, financial records, and client communications to external entities. 

Investigative findings have drawn parallels between DeepSeek and TikTok, the latter having faced a U.S. federal ban over concerns regarding Chinese government access to user data. Unlike TikTok, however, security analysts claim to have found direct evidence of DeepSeek’s potential backdoor access, raising further alarms among cybersecurity professionals. Cybersecurity expert Ivan Tsarynny warns that DeepSeek’s digital fingerprinting capabilities could allow it to track users’ web activity even after they close the app. 

This means companies may be exposing not just individual employee data but also internal business strategies and confidential documents. While AI-driven tools like DeepSeek offer substantial productivity gains, business leaders must weigh these benefits against potential security vulnerabilities. A complete ban on DeepSeek may not be the most practical solution, as employees often adopt new AI tools before leadership can fully assess their risks. Instead, organizations should take a strategic approach to AI integration by implementing governance policies that define approved AI tools and security measures. 

Restricting DeepSeek’s usage to non-sensitive tasks such as content brainstorming or customer support automation can help mitigate data security concerns. Enterprises should prioritize the use of vetted AI solutions with stronger security frameworks. Platforms like OpenAI’s ChatGPT Enterprise, Microsoft Copilot, and Claude AI offer greater transparency and data protection. IT teams should conduct regular software audits to monitor unauthorized AI use and implement access restrictions where necessary. 

Employee education on AI risks and cybersecurity threats will also be crucial in ensuring compliance with corporate security policies. As AI technology continues to evolve, so do the challenges surrounding data privacy. Business leaders must remain proactive in evaluating emerging AI tools, balancing innovation with security to protect corporate data from potential exploitation.
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Hacking
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Cybercrime in 2025: AI-Powered Attacks, Identity Exploits, and the Rise of Nation-State Threats

 


Cybercrime has evolved beyond traditional hacking, transforming into a highly organized and sophisticated industry. In 2025, cyber adversaries — ranging from financially motivated criminals to nation-state actors—are leveraging AI, identity-based attacks, and cloud exploitation to breach even the most secure organizations. The 2025 CrowdStrike Global Threat Report highlights how cybercriminals now operate like businesses. 

One of the fastest-growing trends is Access-as-a-Service, where initial access brokers infiltrate networks and sell entry points to ransomware groups and other malicious actors. The shift from traditional malware to identity-based attacks is accelerating, with 79% of observed breaches relying on valid credentials and remote administration tools instead of malicious software. Attackers are also moving faster than ever. Breakout times—the speed at which cybercriminals move laterally within a network after breaching it—have hit a record low of just 48 minutes, with the fastest observed attack spreading in just 51 seconds. 

This efficiency is fueled by AI-driven automation, making intrusions more effective and harder to detect. AI has also revolutionized social engineering. AI-generated phishing emails now have a 54% click-through rate, compared to just 12% for human-written ones. Deepfake technology is being used to execute business email compromise scams, such as a $25.6 million fraud involving an AI-generated video. In a more alarming development, North Korean hackers have used AI to create fake LinkedIn profiles and manipulate job interviews, gaining insider access to corporate networks. 

The rise of AI in cybercrime is mirrored by the increasing sophistication of nation-state cyber operations. China, in particular, has expanded its offensive capabilities, with a 150% increase in cyber activity targeting finance, manufacturing, and media sectors. Groups like Vanguard Panda are embedding themselves within critical infrastructure networks, potentially preparing for geopolitical conflicts. 

As traditional perimeter security becomes obsolete, organizations must shift to identity-focused protection strategies. Cybercriminals are exploiting cloud vulnerabilities, leading to a 35% rise in cloud intrusions, while access broker activity has surged by 50%, demonstrating the growing value of stolen credentials. 

To combat these evolving threats, enterprises must adopt new security measures. Continuous identity monitoring, AI-driven threat detection, and cross-domain visibility are now critical. As cyber adversaries continue to innovate, businesses must stay ahead—or risk becoming the next target in this rapidly evolving digital battlefield.
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Privacy
AI Gaming AI Model AI Systems AI technology Data Privacy data security Data Technology Microsoft Privacy

Microsoft MUSE AI: Revolutionizing Game Development with WHAM and Ethical Challenges

 

Microsoft has developed MUSE, a cutting-edge AI model that is set to redefine how video games are created and experienced. This advanced system leverages artificial intelligence to generate realistic gameplay elements, making it easier for developers to design and refine virtual environments. By learning from vast amounts of gameplay data, MUSE can predict player actions, create immersive worlds, and enhance game mechanics in ways that were previously impossible. While this breakthrough technology offers significant advantages for game development, it also raises critical discussions around data security and ethical AI usage. 

One of MUSE’s most notable features is its ability to automate and accelerate game design. Developers can use the AI model to quickly prototype levels, test different gameplay mechanics, and generate realistic player interactions. This reduces the time and effort required for manual design while allowing for greater experimentation and creativity. By streamlining the development process, MUSE provides game studios—both large and small—the opportunity to push the boundaries of innovation. 

The AI system is built on an advanced framework that enables it to interpret and respond to player behaviors. By analyzing game environments and user inputs, MUSE can dynamically adjust in-game elements to create more engaging experiences. This could lead to more adaptive and personalized gaming, where the AI tailors challenges and story progression based on individual player styles. Such advancements have the potential to revolutionize game storytelling and interactivity. 

Despite its promising capabilities, the introduction of AI-generated gameplay also brings important concerns. The use of player data to train these models raises questions about privacy and transparency. Developers must establish clear guidelines on how data is collected and ensure that players have control over their information. Additionally, the increasing role of AI in game creation sparks discussions about the balance between human creativity and machine-generated content. 

While AI can enhance development, it is essential to preserve the artistic vision and originality that define gaming as a creative medium. Beyond gaming, the technology behind MUSE could extend into other industries, including education and simulation-based training. AI-generated environments can be used for virtual learning, professional skill development, and interactive storytelling in ways that go beyond traditional gaming applications. 

As AI continues to evolve, its role in shaping digital experiences will expand, making it crucial to address ethical considerations and responsible implementation. The future of AI-driven game development is still unfolding, but MUSE represents a major step forward. 

By offering new possibilities for creativity and efficiency, it has the potential to change how games are built and played. However, the industry must carefully navigate the challenges that come with AI’s growing influence, ensuring that technological progress aligns with ethical and artistic integrity.
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GDPR
AI governance AI Security AI technology CCPA Compliance Cyber Security Data Privacy data security GDPR

The Need for Unified Data Security, Compliance, and AI Governance

 

Businesses are increasingly dependent on data, yet many continue to rely on outdated security infrastructures and fragmented management approaches. These inefficiencies leave organizations vulnerable to cyber threats, compliance violations, and operational disruptions. Protecting data is no longer just about preventing breaches; it requires a fundamental shift in how security, compliance, and AI governance are integrated into enterprise strategies. A proactive and unified approach is now essential to mitigate evolving risks effectively. 

The rapid advancement of artificial intelligence has introduced new security challenges. AI-powered tools are transforming industries, but they also create vulnerabilities if not properly managed. Many organizations implement AI-driven applications without fully understanding their security implications. AI models require vast amounts of data, including sensitive information, making governance a critical priority. Without robust oversight, these models can inadvertently expose private data, operate without transparency, and pose compliance challenges as new regulations emerge. 

Businesses must ensure that AI security measures evolve in tandem with technological advancements to minimize risks. Regulatory requirements are also becoming increasingly complex. Governments worldwide are enforcing stricter data privacy laws, such as GDPR and CCPA, while also introducing new regulations specific to AI governance. Non-compliance can result in heavy financial penalties, reputational damage, and operational setbacks. Businesses can no longer treat compliance as an afterthought; instead, it must be an integral part of their data security strategy. Organizations must shift from reactive compliance measures to proactive frameworks that align with evolving regulatory expectations. 

Another significant challenge is the growing issue of data sprawl. As businesses store and manage data across multiple cloud environments, SaaS applications, and third-party platforms, maintaining control becomes increasingly difficult. Security teams often lack visibility into where sensitive information resides, making it harder to enforce access controls and protect against cyber threats. Traditional security models that rely on layering additional tools onto existing infrastructures are no longer effective. A centralized, AI-driven approach to security and governance is necessary to address these risks holistically. 

Forward-thinking businesses recognize that managing security, compliance, and AI governance in isolation is inefficient. A unified approach consolidates risk management efforts into a cohesive, scalable framework. By breaking down operational silos, organizations can streamline workflows, improve efficiency through AI-driven automation, and proactively mitigate security threats. Integrating compliance and security within a single system ensures better regulatory adherence while reducing the complexity of data management. 

To stay ahead of emerging threats, organizations must modernize their approach to data security and governance. Investing in AI-driven security solutions enables businesses to automate data classification, detect vulnerabilities, and safeguard sensitive information at scale. Shifting from reactive compliance measures to proactive strategies ensures that regulatory requirements are met without last-minute adjustments. Moving away from fragmented security solutions and adopting a modular, scalable platform allows businesses to reduce risk and maintain resilience in an ever-evolving digital landscape. Those that embrace a forward-thinking, unified strategy will be best positioned for long-term success.
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Technology
AI news AI technology Artificial Intelligence Technology

DBS Bank to Cut 4,000 Jobs Over Three Years as AI Adoption Grows

Singapore’s largest bank, DBS, has announced plans to reduce approximately 4,000 temporary and contract roles over the next three years as artificial intelligence (AI) takes on more tasks currently handled by human workers. 

The job reductions will occur through natural attrition as various projects are completed, a bank spokesperson confirmed. However, permanent employees will not be affected by the move. 

The bank’s outgoing CEO, Piyush Gupta, also revealed that DBS expects to create around 1,000 new positions related to AI, making it one of the first major financial institutions to outline how AI will reshape its workforce. 

Currently, DBS employs between 8,000 and 9,000 temporary and contract workers, while its total workforce stands at around 41,000. According to Gupta, the bank has been investing in AI for more than a decade and has already integrated over 800 AI models across 350 use cases. 

These AI-driven initiatives are projected to generate an economic impact exceeding S$1 billion (approximately $745 million) by 2025. Leadership at DBS is also set for a transition, with Gupta stepping down at the end of March. 

His successor, Deputy CEO Tan Su Shan, will take over the reins. The growing adoption of AI across industries has sparked global discussions about its impact on employment. The International Monetary Fund (IMF) estimates that AI could influence nearly 40% of jobs worldwide, with its managing director Kristalina Georgieva cautioning that AI is likely to exacerbate economic inequality. 

Meanwhile, Bank of England Governor Andrew Bailey has expressed a more balanced outlook, suggesting that while AI presents certain risks, it also offers significant opportunities and is unlikely to lead to widespread job losses. As DBS advances its AI-driven transformation, the bank’s restructuring highlights the evolving nature of work in the financial sector, where automation and human expertise will increasingly coexist.
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