Loading story…
Organizations are facing a surge in cyberattacks driven by the rapid adoption of artificial intelligence (AI) and machine learning technologies, with threat actors leveraging these tools to amplify risks across mobile devices, APIs, and cloud environments. Reports highlight that 85% of organizations have experienced an increase in mobile device attacks, with AI-assisted threats such as SMS-phishing and deepfakes becoming more prevalent, while only a minority have dedicated defenses in place. The integration of generative AI and large language models (LLMs) into business applications has led to a proliferation of APIs, introducing new attack vectors like prompt injection and data exfiltration that traditional security tools struggle to detect. Vulnerabilities in widely used AI platforms, such as ChatGPT, have exposed millions of users to risks including data leakage and privacy breaches, underscoring the challenges of securing AI-driven systems.
Despite significant investments in security tools and automation, human behavior remains a leading cause of data loss, with insider risks, misdirected emails, and credential theft persisting as major concerns. The complexity of managing sprawling data across cloud and SaaS platforms further complicates efforts to secure sensitive information. Security leaders are increasingly aware that AI is a double-edged sword—while it offers enhanced efficiency and predictive capabilities, it also empowers adversaries with sophisticated attack methods. The lack of comprehensive AI security policies and the difficulty in aligning AI strategies with business goals have left many organizations unprepared to address the evolving threat landscape, making the need for robust, adaptive security frameworks more urgent than ever.
Mallory correlates global threat intelligence with your attack surface — know if you’re exposed before adversaries strike.
1 event from the most recent confirmed update back to the earliest known activity.
Initial story creation
Vulnerabilities, threat actors, malware, products, organizations, and breaches Mallory has linked to this story.
6 references tracked. Mallory keeps watching after this page renders.
helpnetsecurity.com
Open sourcesecurityboulevard.com
Open sourcecsoonline.com
Open sourcedarkreading.com
Open sourcezimperium.com
Open sourcedarkreading.com
Open sourceMap indicators from this story to your assets and identify affected systems in minutes.
Every observed campaign, victim, and pivot linked to actors named in this story.
Malware, exploits, and IOCs connected to the activity described here.
YARA, Sigma, and Snort rules deployed to your SIEM as soon as they’re published.
Get matching new stories delivered to your team as they break — not the next morning.
Ask questions about this story and take action on the answers.
Other stories Mallory is tracking that overlap on entities, sources, or topic labels.
Security researchers and industry experts are raising alarms about the growing use of artificial intelligence (AI) in both offensive and defensive cybersecurity operations. Attackers are leveraging AI to bypass advanced security controls, as demonstrated by a researcher who used AI to defeat an "AI-powered" web application firewall, and by the emergence of new malware that exploits AI model files and browser vulnerabilities to evade detection and exfiltrate credentials. Meanwhile, defenders are grappling with the proliferation of unsanctioned AI tools in the workplace, the challenge of auditing AI decision-making, and the surge in AI-powered bug hunting, which has led to a dramatic increase in vulnerability discoveries and bug bounty payouts. The risks are compounded by the lack of clear AI usage policies, the potential for data leaks through generative AI tools, and the difficulty in monitoring or controlling how sensitive information is processed and stored by these systems. Industry reports highlight that a significant portion of employees use unauthorized AI applications, often exposing sensitive data without IT oversight, and that prompt injection and model manipulation are now common vulnerability types. The security community is also debating the extent to which ransomware and other attacks are truly "AI-driven," with some reports criticized for overstating the role of AI in current threat activity. As organizations rush to adopt AI for efficiency and innovation, experts urge the implementation of robust governance, continuous monitoring, and red-teaming to anticipate and mitigate the evolving risks posed by both sanctioned and shadow AI systems. The rapid evolution of AI in cybersecurity is forcing a reevaluation of traditional defense models, emphasizing the need for transparency, operational oversight, and adaptive security strategies.
Jun 29, 2026
Artificial intelligence is rapidly transforming both the threat landscape and defensive strategies in cybersecurity, prompting CISOs and security leaders to rethink their approaches. A global study by Gigamon found that 86% of CISOs now view metadata and packet-level data as essential for detecting threats in complex hybrid cloud environments, but 97% admit to making trade-offs that leave visibility gaps. The rise of AI-driven attacks is fueling demand for real-time visibility and observability tools, with 75% of CISOs regarding public cloud as their highest security risk and 73% considering moving workloads back to private clouds. Security teams are investing heavily in AI-specific security tools, with 73% of companies spending over $1 million annually, yet 70% cite the rapid pace of AI development as their top concern. Recent high-profile breaches, such as those at LexisNexis Risk Solutions and McLaren Health Care, illustrate the increasing scale and sophistication of attacks, often amplified by AI. AI is accelerating the reconnaissance phase of attacks, enabling adversaries to map environments and identify vulnerabilities with unprecedented speed and precision, though human direction remains necessary for effective exploitation. The proliferation of AI-generated code, including through practices like 'vibe coding,' introduces new risks as less experienced developers may overlook security fundamentals, leading to insecure applications. Agentic AI systems, which act autonomously or on behalf of users, present urgent challenges in authentication, authorization, and identity management, with experts calling for scalable frameworks and robust credentials to prevent security lapses. CISOs are urged to build security into the design phase of software development, leveraging platform-native controls and enforcing policies like Row Level Security to minimize risk. The integration of AI into security operations is seen as both an opportunity and a challenge, requiring adaptive access solutions, post-quantum cryptography, and continuous monitoring. As AI reshapes digital transformation, organizations must balance the benefits of rapid innovation with the imperative to secure their environments against increasingly sophisticated, AI-powered threats. The consensus among experts is that security must evolve in tandem with AI capabilities, emphasizing proactive risk management, cryptographic agility, and a culture of security awareness across all levels of the organization.
Jun 29, 2026
AI is rapidly transforming the cybersecurity landscape, introducing both significant risks and powerful new defensive capabilities. The widespread adoption of AI tools in the workplace has led to a surge in employees using these technologies, with 65% of people now utilizing AI tools, up from 44% the previous year. However, this increased usage has not been matched by adequate security training, as 58% of employees have received no instruction on AI security or privacy risks. This gap has resulted in sensitive business information, including internal documents, financial data, and client details, being routinely entered into AI systems, raising the risk of data leakage and unauthorized access. Employees express substantial concern about AI's potential to amplify cybercrime, facilitate scams, bypass security systems, and enable identity impersonation, yet only 45% trust companies to implement AI securely. In parallel, AI is being leveraged by both attackers and defenders, with advanced models now capable of simulating and even outperforming human teams in vulnerability discovery and remediation. For example, AI models have been used to replicate major historical cyberattacks in simulation, demonstrating their potential for both offensive and defensive applications. In cybersecurity competitions, AI-driven systems have successfully identified and patched vulnerabilities, sometimes uncovering previously unknown flaws. Organizations like Anthropic have invested in enhancing their AI models to assist defenders, enabling the detection, analysis, and remediation of vulnerabilities in both code and deployed systems. These advancements have led to AI models matching or surpassing previous state-of-the-art systems in cyber defense tasks. At the same time, threat actors are exploiting AI to scale their operations, prompting security teams to develop new safeguards and monitoring techniques. The dual-use nature of AI in cybersecurity underscores the urgent need for robust security awareness training, updated policies, and technical controls to manage the risks associated with AI adoption. As AI continues to evolve, defenders must stay ahead by integrating AI-driven tools into their security operations while remaining vigilant against emerging threats. The current state of AI security is described as precarious, with urgent calls for organizations to address the human and technical factors contributing to risk. The future of cybersecurity will be defined by the ongoing arms race between AI-powered attackers and increasingly sophisticated AI-enabled defenders, making continuous adaptation and investment in AI security essential for organizational resilience.
Jun 29, 2026