Notifications
Clear all
Topic starter
06/06/2026 11:10 am
TL;DR: RSA 2026 conversations, vendor launches, and supporting research converged on a single conclusion: the AI agent context window is the real security perimeter, because more tokens reduce accuracy, dilute instructions, and expand attack surface, according to Zenity and cited research. The governance gap is no longer about model quality, but about who controls what enters context, what persists, and what gets acted on.
NHIMG editorial — based on content published by Zenity: Context Engineering Is Security Engineering. RSA 2026 Made the Case
By the numbers:
- 85% of large enterprises are experimenting with AI agents, but only 5% have moved them into production.
- 97% of organizations with AI-related security incidents lacked proper AI access controls.
Questions worth separating out
Q: How should security teams govern AI agent context windows?
A: Security teams should treat context windows as governed trust boundaries, not as passive text buffers.
Q: Why do AI agents make least privilege harder to enforce?
A: AI agents make least privilege harder because their effective authority depends on what context they can see and act on at runtime, not just on the permissions assigned to the underlying identity.
Q: What breaks when prompt, retrieval, and memory are governed separately?
A: When prompt, retrieval, and memory are governed separately, no one owns the full decision chain.
Practitioner guidance
- Map every context source as a governed access path Inventory system prompts, retrieval feeds, memory stores, tool outputs, and compaction rules as separate trust inputs.
- Constrain context to the minimum necessary tokens Remove unused retrieval sources, trim prompt scaffolding, and reduce memory persistence to the smallest set that still supports the task.
- Correlate posture drift with runtime behaviour Track when agent permissions, connectors, or retrieval sources change and compare those changes with tool calls and output patterns.
What's in the full article
Zenity's full blog post covers the operational detail this post intentionally leaves for the source:
- A deeper walkthrough of the stateful threat engine and how it correlates prompt chains, tool use, and session history.
- Operational examples of real-time exposure visibility when agent permissions, connectors, or MCP servers change.
- The Issues correlation model that ties configuration drift to live behaviour in one investigation.
- Coverage specifics for Microsoft 365 Copilot, Copilot Studio, ChatGPT Enterprise, Salesforce Agentforce, Azure AI Foundry, Google Vertex AI, and ServiceNow.
👉 Read Zenity's analysis of why context engineering is the security perimeter for AI agents →
AI agent context windows: are your controls keeping up?
Explore further
06/06/2026 11:49 am
Context engineering has become the new identity boundary for AI agents. The vendor convergence at RSA 2026 shows that the market is independently identifying context as the primary control point, not the model. That is a material shift for identity governance because the relevant question is no longer only who the agent is, but what instruction environment it is operating inside. Practitioners should treat context as a governed authorization surface, not as an implementation detail.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete compliance and investigation blind spot.
A question worth separating out:
Q: How can organisations tell whether AI agent controls are actually working?
A: The best signal is whether the agent’s behaviour stays stable when context changes. If the same agent becomes less accurate, more permissive, or more erratic as retrieved data grows or permissions drift, the controls are not working well enough. Good governance produces predictable behaviour under constrained context, not just clean scan results.
👉 Read our full editorial: Context engineering is the security perimeter for AI agents
