Topic starter
02/08/2026 11:26 am
TL;DR: AI agents are expanding enterprise attack surfaces through prompt injection, memory poisoning, privilege escalation, and MCP-connected tooling, according to Akto. Static app security controls do not map cleanly to systems that choose tools, recall context, and act at runtime without human approval.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Agent Security Risks in 2026: Runtime Protection & Security Best Practices”.
Key questions
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Q: Why do over-permissioned AI platform identities increase breach risk?
A: Because they turn a single workload compromise into an identity pivot.
Q: What are the signs that prompt injection is influencing an AI coding assistant?
A: Look for unexpected tool calls, unexplained file reads, outbound requests that do not match the developer’s task, or the agent acting on instructions that only appear inside code comments, README files or dependency content.
Practitioner guidance
- Define runtime authorization boundaries Separate what an agent may request from what it may execute during the same session, and require explicit approval for any tool path that can reach production data or destructive actions.
- Inventory external connectors and tools Map every API, MCP connector, webhook, database, and code execution path the agent can reach, then remove anything not needed for the current task scope.
- Protect persistent memory stores Review long-term memory, vector stores, and retrieval sources for write access, poisoning risk, and unaudited context reuse across sessions.
Bottom line: AI agents create a governance problem because they can choose tools and act during execution, which leaves static app controls looking at the wrong moment.
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02/08/2026 12:43 pm
Runtime control gaps, not model sophistication, are the core security problem: This article shows that the decisive risk is not whether an agent sounds intelligent, but whether it can choose tools and act after initial authorisation. Controls built for static request-response systems fail once the security boundary moves into the middle of a session. Practitioners should treat runtime as the primary governance surface.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should teams respond when AI agents use third-party tools and MCP connections?
A: Teams should treat those connections as access-bearing dependencies, not as ordinary integrations. That means approving them before connection, recording them in a registry, and validating what data they can reach and what actions they can trigger. If the dependency can modify enterprise state, it belongs in the identity governance process.
👉 Read our full editorial: AI agent security risks in 2026 expose runtime control gaps