Agentic AI has moved past experiments and into real deployment, which means CISOs must now secure systems that take autonomous actions, chain outputs across tools, and slip past the familiar control points we rely on. This report translates Forrester’s Agentic AI Enterprise Guardrails for Information Security (AEGIS) framework into execution with a clear map of the AEGIS technology ecosystem, aligned to where controls operate. It shows CISOs how to turn AEGIS into an operating model with accountable ownership, a minimum baseline for production, and a 180-day plan. The result is a practical playbook that speeds AI adoption while preserving assurance.

In nine days this month, OpenAI and Anthropic disclosed that their own models escaped safety evaluations and reached production systems at four other companies. No model malfunctioned — the governance did. Responsible AI now has to govern what agents do, not only what models decide, and that takes a responsible AI deployment policy.

From regulatory scrutiny to reputational damage and failed deployments, “reckless AI” is no longer a theoretical risk. In fact, leading organizations are proving that trusted AI is the foundation for scaling it successfully. Moderated through real-world scenarios, the session will examine how governance, accountability, and trust frameworks directly impact AI success. This conversation will be hosted by Enza Iannopollo, VP and principal analyst at Forrester, and will include open discussion with participants, so please bring your questions and experiences to share with the group. Questions for discussion:Based on your experience, what is the status of AI regulation around the world, and what do you expect to see next? Who owns AI governance and risk management in your organization today, and how is that responsibility structured? According to your experience, do you believe that AI governance alone is enough to build trustworthy AI? What else is needed? Peer discussions are available exclusively to Forrester Decisions VIP Leader and Leader license holders for their own participation.

Designing, governing, and delivering AI responsibly is the foundation of trusted AI. If your customers don’t trust AI, they will question every single AI-powered experience; companies that fail to build trust also risk seeing their AI fail. This template helps organizations design and execute a roadmap for responsible AI. It takes into account organizations’ current maturity and maps the key actions to take across responsible AI strategy, people (e.g., stakeholders and talent), processes, and technology. Use this tool to assess your responsible AI maturity and chart the path forward.

Uncertainty and change aren’t new for cybersecurity; they are its operating system. Security leaders must now handle a threat landscape influenced by two forces that don’t politely take turns: breakneck AI innovations and geopolitical volatility. This report lays out the five threats that will impact security teams the most in 2026: near-autonomous attacks from nation-states, concerns over AI agent threats, non-negotiable AI software supply chains, provenance and IAM risks of AI agents, and digital sovereignty spanning regions and tech stacks. Security and risk professionals can use this report to understand, prioritize, and respond to these threats in 2026.

Accelerate innovation with deeper insights about why AI security and trust are essential, when these technologies will pay off, and how organizations are using them today. AI security and trust technologies protect AI systems against emerging risks while ensuring transparency, governance, and compliance across the AI lifecycle. In this webinar, we share the latest expert insights, operational guidance, and key steps you and your team can take. Join us to learn how firms can deploy AI safely, reduce risk, and enable faster adoption at scale. Key takeaways: Gain an understanding of how peers secure and govern enterprise AI systems.Identify AI use cases that demand stronger trust, security, and oversight.Learn the critical steps to implementing scalable AI guardrails.Target audience level: all levels

The Forrester data and AI governance model connects data and AI governance as complementary and interdependent disciplines that must work together to augment business outcomes of assurance, control, enablement, intelligence, and trust. Data, technology, risk, and AI leaders must build trusted data foundations and governed AI capabilities and synergy to shift governance from a limiting force to one that unleashes innovation by supporting trust, assurance, control, enablement, and intelligence across the enterprise.

On May 1, 2026, six national cybersecurity agencies — CISA, the NSA, Australia’s ASD ACSC, and their counterparts in Canada, New Zealand, and the UK — published “Careful adoption of agentic AI services.” This joint guidance is the first coordinated multigovernment security guidance specifically targeting agentic AI systems, and it carries the full weight of […]

US companies are drowning in AI rules. With a labyrinth of conflicting state laws and no single federal requirement, even the most responsible innovators are struggling to stay afloat. California’s landmark Transparency in Frontier Artificial Intelligence Act proved that states can regulate AI without killing innovation, but it also underscores a hard truth: The current […]

If you are a CISO at a critical-infrastructure organization in Bulgaria, France, Luxembourg, the Netherlands, Poland, Spain, or Sweden, your Critical Entities Resilience (CER) Directive enforcement clock just shortened. On May 7, 2026, the European Commission referred all seven member states to the Court of Justice of the European Union for failing to transpose the CER Directive more […]