Treat the agent as a privileged runtime with untrusted input, not as a chat interface. Limit tool access, shorten session scope, and remove at least one high-risk property such as external write access or access to sensitive systems. Then add monitoring or enforcement at the action boundary so the control evaluates what the agent actually does, not just what it reads.
Why This Matters for Security Teams
An AI agent that can read untrusted text and then take actions is not just a content processor, it is an execution path. That changes the risk from information exposure to manipulation of decisions, approvals, and downstream systems. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point to the same core issue: input trust cannot be assumed when the system has tool access.
Security teams often misclassify these agents as an application layer problem, then discover that prompt injection, malicious document content, or adversarial instructions can steer the agent into unintended actions. The practical concern is not whether the model “understands” the text, but whether the surrounding control plane can prevent unsafe tool calls, credential use, or data disclosure once untrusted content is in the context window. That is why governance must focus on authority boundaries, not just content filtering.
In practice, many security teams encounter agent abuse only after an untrusted email, ticket, or document has already caused an approved action, rather than through intentional testing of the action boundary.
How It Works in Practice
Effective governance starts by treating the agent as a privileged runtime with constrained authority. That means separating read access from write access, scoping sessions tightly, and making tool use explicit rather than implicit. A safe design does not rely on the model to “behave”; it enforces limits before, during, and after each action. The MITRE ATLAS adversarial AI threat matrix is useful for mapping how prompt injection, indirect instruction attacks, and data poisoning can influence model behaviour, while CSA MAESTRO agentic AI threat modeling framework helps teams reason about agent workflows, tools, and trust zones.
Operationally, teams should implement a control pattern with four parts:
- Restrict the agent to the minimum set of tools and destinations required for the task.
- Require policy checks at the action boundary so a tool call is validated before execution.
- Separate untrusted input from system instructions, secrets, and privileged context.
- Log prompts, tool invocations, approvals, and refusals so risky behaviour can be investigated.
For higher-risk environments, the agent should also have a human approval step for writes, financial operations, administrative changes, or any action that is hard to reverse. This is especially important when the agent can process third-party content such as support tickets, web pages, attachments, or messages that may contain hidden instructions. The governance objective is to make unsafe action impossible by design, not merely detectable after the fact. These controls tend to break down when agents share long-lived sessions across multiple systems because a single poisoned input can influence later privileged actions.
Common Variations and Edge Cases
Tighter action controls often increase latency and operational overhead, requiring organisations to balance autonomy against blast-radius reduction. That tradeoff becomes more visible when the agent must work across multiple tools, business units, or data classifications. There is no universal standard for the exact approval threshold yet, so current guidance suggests matching control strength to the reversibility and impact of the action.
One common edge case is retrieval-augmented workflows. If the agent reads untrusted text from search results, shared drives, or customer content, the retrieval layer itself becomes part of the attack surface. Another is semi-autonomous workflows where the model drafts an action but a person approves it. That reduces risk, but only if the approver can see the source content, the proposed action, and any policy violations clearly enough to make a meaningful decision.
Security teams should also distinguish between internal enterprise agents and externally exposed agents. Internet-facing agents, customer service assistants, and agents that process third-party uploads need stricter isolation, stronger content validation, and narrower write permissions. The most difficult cases are those that combine sensitive systems, persistent memory, and weak transaction boundaries, because unsafe state can survive across sessions and compromise later decisions. For broader control mapping, the NIST Cybersecurity Framework 2.0 provides useful language for governance, protection, detection, and response.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI RMF, NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Agentic AI risks center on untrusted input influencing tool use and actions. | |
| NIST AI RMF | GOVERN | Governance is needed to assign accountability for autonomous agent behaviour. |
| MITRE ATLAS | AML.TA0001 | Prompt injection and manipulation map to adversarial AI attack techniques. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access restriction are central to limiting agent blast radius. |
| NIST SP 800-53 Rev 5 | AC-6 | Least privilege and authorization enforcement support safe action boundaries. |
Apply agent-specific controls to limit tool scope, isolate context, and gate actions before execution.
Related resources from NHI Mgmt Group
- How should security teams govern AI agents that act faster than directory enrollment?
- How should security teams govern AI agents that can inspect and act inside browser-based simulators?
- How should security teams govern AI agents that can act within permission but outside purpose?
- How should security teams govern access when bots and AI agents act like non-human identities?
