risk management

The 7 Biggest Issues CISOs and Security Teams Face Today

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CISOs today face escalating challenges including increasing AI-enabled cyberattacks, expanding attack surfaces, and amplified stress levels, with 71% citing the complex threat landscape as their main stressor. Key issues include shifting focus from prevention to resilience and recovery, aligning security programs to organizational needs, integrating and securing AI technologies and infrastructure, addressing critical cybersecurity skills gaps, adopting agile security practices to keep pace with rapid threats, and maintaining flawless execution of fundamental security principles. Security leaders must develop tailored strategies and invest in people, processes, and AI security tools to manage these evolving risks effectively.

https://www.csoonline.com/article/4232208/the-7-biggest-issues-cisos-and-security-teams-face-today.html

Unclear AI Risk Ownership Could Leave CISOs in Familiar Scapegoat Role

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Lack of clear ownership over AI risk and governance creates a risk that Chief Information Security Officers (CISOs) will be unfairly held accountable for AI security incidents without corresponding authority to control AI systems. Experts emphasize that while CISOs should oversee AI security boundaries—including reviewing AI deployments, access controls, and monitoring—AI risk management must also involve business, legal, and technology leaders, reflecting AI’s cross-functional impact. To avoid reactive blame and governance gaps, organizations need to define and connect responsibilities across functions proactively before incidents occur.

https://www.techtarget.com/cybersecurity/news/366651977/Unclear-AI-risk-ownership-could-leave-CISOs-in-familiar-scapegoat-role

6 Guidelines for Governing AI

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Sravan Vadigepalli, leading enterprise AI strategy at Lowe’s, outlines six guidelines for governing AI systems that emphasize keeping humans involved in decision-making through principles rather than rigid rules. He stresses the need to encode corporate values as machine-readable governance layers, implement confidence-based human oversight (“trust thermostat”), and ensure context integration for AI reasoning, enabling organizations to shift from directly executing tasks to overseeing AI-driven processes with human judgment focused on exceptions. This governance approach aims to bridge the gap between AI experimentation and scalable business value by redefining roles around setting intent, managing risk, and embedding culture in AI operations.

https://spectrum.ieee.org/6-guidelines-governing-ai

Why CISOs Struggle to Answer the Board’s Three Hardest Questions, and How to Fix the Report

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CISOs often struggle to confidently answer boards’ key questions on security posture, financial exposure, and improvement because relevant data is siloed across multiple tools that lack shared context. A proposed solution involves adopting a unified intelligence layer that correlates identity, cloud, endpoint, and SaaS data into attack paths to critical assets, enabling reports focused on exposure and financial risk trends instead of activity metrics. This approach helps security leaders provide measurable risk reduction insights aligned with business priorities and facilitates more effective board communication and remediation prioritization.

https://thehackernews.com/2026/10/why-cisos-struggle-to-answer-boards.html

If AI Makes the Decision, Who Owns the Consequence?

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As AI increasingly shapes operational decisions rather than merely assisting tasks, organizations face a governance challenge in clearly defining who holds authority and accountability for AI-driven outcomes. Boards should focus on identifying material AI-supported decisions, mapping who can authorize, oversee, and accept consequences for these decisions, and demanding evidence that authority boundaries are respected and exceptions managed. Effective AI governance requires moving beyond inventories toward accountability frameworks that trace decision authority and ensure humans remain responsible for consequences.

https://www.cio.com/article/4226780/if-ai-makes-the-decision-who-owns-the-consequence.html

Why This CIO Won’t Let an AI Agent Take the Blame

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Brigitte Larivière, CIO at Bombardier, emphasizes human accountability in automation by requiring a named person responsible for each AI agent deployed, ensuring clear ownership and measurement of outcomes. She prioritizes automating IT tasks—aiming for 80% automation of outsourced infrastructure work by 2027—while maintaining on-site support to directly assist manufacturing workers and fostering controlled citizen development with approved tools. Larivière also highlights cybersecurity as a critical concern amid rapid technological changes, focusing on transparency and risk management as automation and AI adoption accelerate.

https://www.digitaljournal.com/article/why-this-cio-wont-let-an-ai-agent-take-the-blame/

Risk Management Isn’t an Insurance Decision, It’s a Leadership Decision

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Powell Brown, CEO of Brown & Brown, emphasizes that risk management is a leadership responsibility that requires continuous engagement rather than treating insurance as a transactional annual renewal. Effective risk management involves understanding exposures, preparing for diverse scenarios, and integrating risk considerations into business operations to protect organizational assets amid evolving threats like cyberattacks, AI governance challenges, and geopolitical uncertainty. Brown advocates for specialized, advisory-driven risk strategies tailored to each industry to help leaders make informed decisions and strengthen their organizations proactively.

https://www.chicagobusiness.com/crains-content-studio/publishing-partner/ccb-risk-management-insurance-leadership-brown/

3 Best Practices to Bridge the AI Strategy and Governance Divide

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To bridge the divide between AI strategy and governance, organizations should first move risk tiering upstream by integrating risk assessment into early portfolio and funding decisions to avoid costly oversight later. Second, CIOs should focus on owning observability by building transparent AI inventories and telemetry systems that assign clear accountability to business units rather than IT alone. Third, making technical governance artifacts part of the AI platform—rather than standalone policies—ensures governance is embedded in operational systems, promoting scalable oversight aligned with business objectives.

https://www.cio.com/article/4214897/3-best-practices-to-bridge-the-ai-strategy-and-governance-divide.html

Business Security Is a Myth. Here’s Why Only the Paranoid Survive.

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The article argues that true business security is unattainable, and the greatest threat to a business is stasis rather than vulnerability. It emphasizes that businesses should prioritize agility and constant adaptation over attempts at static security, suggesting that only those organizations maintaining a paranoid, vigilant mindset can survive in a rapidly changing environment.

https://www.entrepreneur.com/building-a-business/business-security-is-a-myth-heres-why-only-the-paranoid-survive

Five Questions CIOs Need to Answer Before AI Moves to Production

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Before deploying AI into production, CIOs should ask critical questions about data quality and governance, integration with existing IT infrastructure, and the scalability and reliability of AI models. They must also evaluate security and compliance implications and ensure alignment with business objectives to maximize AI’s operational impact. Addressing these factors helps organizations manage risks and achieve effective, sustainable AI adoption.

https://www.genpact.com/insight/five-questions-cios-need-to-answer-before-ai-moves-to-production

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