risk management

What CISOs Should Know About AI Runtime Security

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CISOs should focus on AI runtime security, which involves protecting AI systems while they are actively operating to prevent data leaks, compliance breaches, and misuse of AI as an attack tool. Key challenges include rapidly evolving AI technologies, expanding enterprise use cases, and a lack of AI-specific security tools, necessitating zero-trust principles that control identity, access, inputs, outputs, and monitor AI behavior continuously. Implementing these measures requires new cybersecurity tooling and prioritizing investments based on organizational AI risk assessments.

https://www.techtarget.com/searchsecurity/tip/What-CISOs-should-know-about-AI-runtime-security

Your CISO Is Becoming a Safety Architect (Whether They Know It or Not)

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The traditional role of the CISO is shifting from defending against external human attackers to managing risks posed by autonomous AI agents operating inside organizations. These AI agents act at machine speed with broad permissions, creating new safety challenges as their failures resemble industrial accidents driven by complexity and unpredictability rather than malicious intent. To address this, CISOs must adopt a safety architecture approach focused on observability and pattern-driven monitoring to ensure reliable and accountable AI behavior within enterprise environments.

https://www.scworld.com/perspective/your-ciso-is-becoming-a-safety-architect-whether-they-know-it-or-not

Forget Data Leakage: Shadow AI’s Real Threat Is Access Control

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Shadow AI in enterprises has evolved from a data leakage issue to a complex access control challenge, as AI agents increasingly act autonomously with broad permissions on critical systems. These agents, created rapidly across departments via various tools, can read, write, and modify data using inherited credentials, often without clear ownership or oversight, posing significant security risks beyond traditional controls. Effective governance requires continuous discovery, ownership assignment, scoped access, and automated lifecycle management of AI agents to prevent unauthorized actions and exposure within organizational environments.

https://thehackernews.com/2026/06/forget-data-leakage-shadow-ais-real.html

5 AI Risk Management Frameworks for Shoring up Key Gaps

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A new generation of AI-specific risk management frameworks has emerged to address gaps in traditional governance, security, and compliance models, helping organizations identify AI risks, implement controls, and demonstrate responsible AI use. Five notable frameworks include the ISO/IEC 42001 AI Management System, the NIST AI Risk Management Framework, ENISA’s AI Cybersecurity Practices, ISO/IEC 23894 guidance on AI risk, and Google’s Secure AI Framework (SAIF), each focusing on different aspects like governance, lifecycle risk management, cybersecurity, or operational security. These frameworks are complementary and vary in complexity and focus, with organizations advised to select ones that align best with their AI risk challenges and maturity level.

https://www.csoonline.com/article/4185917/5-ai-risk-management-frameworks-for-shoring-up-key-gaps.html

5 Things CIOs Must Do as Sovereignty Becomes a Design Constraint

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CIOs are adapting to rising geopolitical tensions and data sovereignty requirements by treating geography as a core architectural constraint, shifting from global efficiency to multi-jurisdiction resilience, and classifying workloads based on sovereignty risk. They are designing platforms for workload portability and exit flexibility, while extending sovereignty considerations to data access at the edge and endpoints, reflecting a broader shift from cost-driven to continuous risk management in enterprise technology strategy.

https://www.cio.com/article/4178779/5-things-cios-must-do-as-sovereignty-becomes-a-design-constraint.html

The AI Shift in Cyber Risk: Why Leaders Must Act Now

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The Five Eyes cyber security agencies warn that rapid advancements in AI are transforming cyber risks by increasing the speed, scale, and complexity of attacks. They urge organizational leaders to prioritize foundational cyber security practices like reducing attack surfaces, accelerating patching, addressing legacy systems, strengthening access controls, and preparing incident response plans. Integrating AI into defensive strategies is essential, but cyber resilience must be embedded in core business operations to maintain continuity and market trust amid evolving threats.

https://www.ncsc.gov.uk/news/the-ai-shift-in-cyber-risk-why-leaders-must-act-now

Stop Your Legacy Infrastructure From Hijacking Your AI Agents

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Enterprises deploying AI agents risk compromise when attackers exploit vulnerabilities in legacy infrastructure that these agents depend on, such as unpatched servers, misconfigured Active Directory permissions, and excessive cloud access privileges. Security programs must adopt an exposure management approach that maps and secures the entire attack path—from network and identity layers through cloud infrastructure to AI agent resources—to prevent attackers from leveraging inherited permissions and legacy exposures to hijack AI agents.

https://thehackernews.com/2026/06/stop-your-legacy-infrastructure-from.html

Risk Management Systems Should Be Constantly Evolving, FDA Official Says

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FDA official Keisha Thomas emphasized at the RAPS Quality Conference that medical device risk management systems must be dynamic and continuously evolving to address firm-specific risks across all quality management system (QMS) areas. The FDA's new risk-based inspection program under the Quality Management System Regulation (QMSR) focuses on comprehensive compliance rather than conformity, highlighting common citations related to insufficient integration of risk management into decision-making and a decoupling of corrective and preventive actions. The agency also indicated that firms participating in the Medical Device Single Audit Program (MDSAP) may still face FDA inspections if risk signals warrant additional oversight.

https://www.raps.org/resource/risk-management-systems-should-be-constantly-evolving-fda-official-says.html

Gartner Security Summit 2026: Huntress 5 Key Takeaways

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At the Gartner Security & Risk Management Summit 2026, the key insight emphasized was that effective security is an ongoing journey focused on resilience, honest risk assessment, and rapid recovery rather than chasing every emerging trend or technology. Organizations succeeding in the evolving threat landscape prioritize building a strong foundation in identity management, control effectiveness, and operational reality to enhance their ability to withstand and respond to incidents. This pragmatic approach highlights that security is a continuous process centered on adaptability and resilience in the face of challenges, especially with the rise of AI-driven threats.

https://www.huntress.com/blog/key-takeaways-gartner-security-risk-summit

CIOs: Tear Down the Wall Between Resilience and Data Security

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AI is exposing the longstanding separation between organizational resilience—focused on system uptime—and data security—focused on protecting information—as no longer sustainable. CIOs are urged to integrate these functions by inventorying and governing unstructured data, automating compliance controls to keep pace with AI-driven threats, and establishing clear audit trails for AI agent actions to meet regulatory demands. This unified approach is essential for enabling enterprise innovation while maintaining trusted data and system recoverability in the evolving AI risk landscape.

https://www.cio.com/article/4179381/cios-tear-down-the-wall-between-resilience-and-data-security.html

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