Topic starter
24/06/2026 10:59 pm
TL;DR: Prompt injection, API abuse, and data leakage happen while models, apps, and agents are live, according to WitnessAI. Runtime policy and observability have to act during execution if AI behavior is to stay inside its intended boundary.
Editorial analysis by NHI Mgmt Group, based on content published by WitnessAI: “AI Runtime Security: Protecting AI Models from Real-Time Threats”.
Key questions
Q: Why do AI agents create a governance problem for IAM teams?
A: AI agents create a governance problem because they authenticate and act as autonomous software entities with tool access.
Q: What signals show that AI runtime controls are failing?
A: Warning signs include unexplained tool usage, access to data outside the expected workflow, repeated policy overrides, and behavioural drift across sessions.
Q: What breaks when AI usage is governed only with static allowlists and access rules?
A: Static allowlists and access rules control whether a user can reach an AI tool, but they do not govern what happens inside the live interaction.
Practitioner guidance
- Define runtime policy boundaries for AI systems Specify which prompts, tool calls, outputs, and data sources are allowed during live execution, then enforce those rules at runtime instead of relying only on pre-approved access roles.
- Instrument observability on AI inputs and outputs Collect telemetry for prompts, model responses, API calls, and connected workflow actions so security teams can detect unsafe behaviour while the session is still active.
- Restrict AI access to the minimum live task Limit each model or agent to the smallest action set needed for the current task, then separate access by workflow stage rather than granting broad standing permissions.
Bottom line: AI runtime security addresses the gap between pre-authorised access and what models, agents, and APIs do while they are live.
Glossary terms: AI Runtime Security, Trace Observability, Prompt Injection (Agentic), Dynamic Access Control
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This topic was modified 1 day ago by NHI Mgmt Group
25/06/2026 7:49 am
Static access control is the wrong control plane for AI runtime risk. Once an AI system is live, the security problem is no longer simply whether it was authorised to start. The relevant question becomes what it can do with prompts, APIs, and connected data sources while it is running. For IAM and PAM teams, that means runtime enforcement has to complement provisioning-time policy, not wait behind it.
A few things that frame the scale:
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: How should security teams govern AI agents that can invoke multiple tools in one session?
A: Security teams should govern AI agents as decision-making identities, not just tool users. That means defining tool access, context scope, and escalation limits together, then monitoring the full execution chain for unexpected combinations of actions. If those controls are split across teams or policies, the agent can move faster than review cycles and create impact before anyone intervenes.
👉 Read our full editorial: Runtime AI security requires controls that act during execution
This post was modified 1 day ago by NHI Mgmt Group