How can organisations reduce AI agent runtime risk without stopping adoption?

Why This Matters for Security Teams

AI agents create runtime risk because they do not follow fixed, human-like access patterns. Once an agent can chain prompts, tools, memory, and external data, the main question is no longer whether it has access, but what it can do at the moment a task is executed. That is why static role design and one-time approvals often miss the real failure mode: an apparently narrow permission set can still produce broad, unintended actions.

This is especially visible in agentic systems where connectors reach SaaS, code repositories, ticketing platforms, and internal APIs. Guidance from OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward runtime controls, not blanket prohibition, as the practical path forward. NHI Management Group’s OWASP NHI Top 10 research also shows that agent failures are often really identity and connector failures under another name. The operational issue is not adoption itself, but unchecked authority at execution time. In practice, many security teams encounter abuse only after an agent has already combined legitimate tools in an illegitimate sequence, rather than through intentional design review.

How It Works in Practice

The most effective way to reduce runtime risk is to treat the agent as a workload with constrained, observable authority. That starts with workload identity, short-lived credentials, and policy decisions made at request time. Instead of issuing broad, long-lived secrets, organisations increasingly use ephemeral tokens, per-task grants, and connector-specific permissions that expire when the task ends. This limits blast radius when the agent behaves unexpectedly.

Runtime monitoring should then validate what the agent is trying to do, not just who it is. For example, policy engines can inspect the requested tool, target resource, data sensitivity, and session context before allowing an action. This is where intent-based authorisation becomes useful: the decision is based on the current goal, not a static role. The emerging pattern aligns with CSA MAESTRO agentic AI threat modeling framework, which emphasises control of orchestration, tool use, and trust boundaries.

• Use just-in-time credentials for each high-risk task.
• Bind tool access to the minimum resource scope the task requires.
• Log tool calls, prompt context, and policy denials for continuous review.
• Block unsafe connector combinations, even if each connector is individually approved.
• Revoke access automatically when a task completes or deviates from expected intent.

For practical implementation, the strongest pattern is policy-as-code combined with strong telemetry. Real-time checks can be expressed through OPA, Cedar, or equivalent controls, while identity proof can be anchored in workload identity systems such as SPIFFE/SPIRE or OIDC-backed service tokens. NIST’s AI governance guidance and NHI-focused incident analysis from Analysis of Claude Code Security both reinforce the same point: runtime controls matter because autonomous systems can discover new paths faster than pre-approved access reviews can track them. These controls tend to break down in highly interconnected environments where many plugins share the same backend credentials and policy enforcement is inconsistent across tools.

Common Variations and Edge Cases

Tighter runtime control often increases latency and operational overhead, so organisations have to balance safety against user experience and agent throughput. That tradeoff becomes sharper when agents are embedded in developer workflows, customer support, or security operations, where even small delays can create pressure to loosen controls.

Best practice is evolving, and there is no universal standard for how much autonomy a production agent should retain. Some environments can safely use allowlisted actions and strict human approval for sensitive steps. Others need more fluid control, such as risk scoring, step-up verification, or session-based restrictions that change as the task unfolds. The right answer depends on the connector surface, data sensitivity, and whether the agent can reach destructive systems like code deployment or record deletion.

Edge cases matter most when multiple agents cooperate or share memory. A single agent may look well-governed, but a multi-agent pipeline can create privilege chaining that no individual policy review anticipated. This is why current guidance suggests assessing the entire orchestration path, not only the primary model. NHI Management Group’s reporting on CoPhish OAuth Token Theft via Copilot Studio and Gemini AI Breach for Google Calendar shows how prompt injection and connector abuse can turn ordinary integrations into compromise paths. Runtime governance helps, but it works best when paired with red teaming, connector inventories, and rapid revocation procedures.

Related resources from NHI Mgmt Group

• When do AI agent credentials create more risk than they reduce?
• How can organisations reduce AI agent blast radius without blocking adoption?
• How can organisations reduce shadow AI risk without blocking adoption?
• How can organisations reduce shadow AI risk without slowing adoption?