Forrester Just Told Every CISO to Prioritize AI Governance. Here's What That Actually Requires

Forrester's 2026 Security Budget Planning Guide has a clear directive for security leaders: broaden AI and machine learning security across the enterprise. Not next year. Now.

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Generative AI has moved out of experimental use and into the core systems your organization runs on: productivity suites, CRM platforms, service tools, finance workflows. Every one of those integrations widens your attack surface. AI risk doesn't wait for a known exploit. It emerges from how employees use these tools every day, in ways most security teams currently cannot see.

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The harder question is what AI governance actually requires in practice, and why most organizations aren't there yet.

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The Gap Between Adoption and Control

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According to IBM and the Ponemon Institute, 63% of organizations have no AI governance policies in place to manage AI or prevent shadow AI from spreading. Of the organizations that reported an AI-related security incident last year, 97% acknowledged they lacked proper AI access controls.

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That's not a policy gap. It's a visibility gap. Employees aren't waiting for approved tools. They're using whatever works, across any browser or application, often without thinking about what happens to the data they share. Over 70% of employees now use generative AI tools at work, many sharing sensitive business data in prompts, while more than 80% of organizations have little to no visibility into that activity.

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The cost is real. Organizations without AI governance take an average of 181 days to identify a breach. By then, the damage is done.

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Why Traditional Tools Miss This

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Most security stacks were built for a different threat model. Network perimeter controls, browser-level monitoring, and legacy DLP tools cannot see what happens at the endpoint when an employee interacts with an AI application. Plugin-based approaches tied to a single browser create coverage gaps the moment someone switches browsers or uses a desktop app, and maintaining plugins across multiple environments adds overhead while still leaving blind spots.

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Blocking AI tools entirely isn't the answer either. It drives usage underground, kills productivity, and leaves security teams with less visibility than before. The goal is to protect what data employees are sharing and where it's going, not to stand between them and the tools they need to work.

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Forrester also flags machine identity management as a growing priority. AI agents and automated workflows are generating identities faster than humans can track or manage, inheriting user-level permissions and acting across systems autonomously. A governance framework that accounts for human behavior but ignores machine activity is incomplete by design.

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What Real AI Governance Looks Like

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Forrester's directive is to secure AI across models, data, applications, and user identities. Here is what executing on that actually requires:
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Endpoint-level visibility, browser and application agnostic. Governance has to happen where the risk originates, at the device, before data moves. That means coverage across every browser, every desktop app, and every AI tool an employee might use, not just the ones IT anticipated. Plugin-based approaches tied to a single browser create exactly the blind spots shadow AI thrives in.
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Behavioral context, not just activity logs. Knowing an employee opened an AI tool is not the same as understanding what they shared, what data was involved, and whether the behavior represents real risk. Intent-based behavioral detection separates meaningful signals from noise and reduces the false positive burden on already stretched security teams.
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Protection that doesn't slow people down. Employees are going to use AI. Governance that simply blocks tools creates friction, drives shadow usage, and turns security into the obstacle. The right approach protects the data, governing what gets shared and where it goes, while letting employees use the tools they need. Real-time guidance at the moment of risk is more effective than blanket restrictions.
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Coverage across both generative and agentic AI. Governance frameworks scoped only to tools like ChatGPT are already behind. Agentic AI systems are moving into production environments, acting autonomously across workflows with broad access and limited oversight. Governance has to extend to both.
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Unified visibility across insider risk, AI usage, and data movement. These are not separate problems. The same platform that detects data exfiltration should also surface shadow AI usage and the behavioral signals that precede a breach, whether the risk comes from a departing employee, a negligent user, or an unsanctioned AI tool.
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The Window to Get Ahead of This Is Closing

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Forrester's 2026 guidance reflects what the data has been showing for the past two years: AI adoption has outpaced governance, and the cost of that gap is growing. The Ponemon Institute's 2026 Cost of Insider Risks report puts the average annual cost of insider risk at $19.5 million, up 20% over two years, with shadow AI and ungoverned AI tools cited as a primary driver of new exposure.

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Security leaders who move now have the advantage. The ones who wait will be governing AI in the middle of an incident, not ahead of it.

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Unlike traditional DLP, browser isolation, or API-based AI governance tools, InnerActiv operates directly at the endpoint, giving organizations real-time visibility and control over AI usage, sensitive data interactions, and user behavior across virtually any browser, desktop application, or AI tool before data leaves the device. By combining AI Usage & Control with behavioral risk intelligence and insider threat detection, InnerActiv gives security teams the visibility, context, and inline control needed to identify and stop risky behavior before damage occurs.

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If Forrester's directive is on your 2026 list, we can help you execute it.

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