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Managing risk from agentic AI means governing the full lifecycle of systems that can plan, use software tools, and take actions—not just checking model outputs before launch. Start by defining what the agent is allowed to do, mapping its tools and data, testing its behavior, and monitoring its actions after deployment. NIST’s voluntary AI Risk Management Framework (AI RMF) offers a useful lifecycle structure, but it is not an agent-specific safety checklist or a guarantee of safe operation.
How can organizations manage risks from agentic AI?
Use a continuous cycle: establish accountability, understand the deployment context, evaluate relevant risks, and manage them throughout operation and retirement. This is especially important when a model’s output can trigger effects in another system.
“Agentic AI” does not have one universally settled definition in the sources cited here. NIST describes agentic AI as systems functioning as autonomous agents that can independently make decisions, learn from interactions, and adapt to changing environments. Its 2026 request for information describes AI agent systems as capable of planning and taking autonomous actions that affect real-world systems or environments. In practice, assess the system’s actual autonomy, connected tools, data access, and possible consequences rather than relying on the label alone.
Current agent systems can combine a general-purpose model with software scaffolding that manipulates tools. That means an agent’s risk depends not only on what it says, but also on the permissions it has, the systems it can reach, and the context in which it operates. NIST’s agentic AI overview and its 2025 account of tool use describe this shift from text output to actions through software capabilities.
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Govern: establish ownership and authority
Assign people accountable for the agent’s risks and decisions. Define acceptable uses, prohibited uses, escalation paths, and which decisions must remain with a human. Governance should apply across design, deployment, monitoring, and change—not operate as a one-time approval.
Map: document the system and its setting
Record the intended use, deployment context, users and affected parties, system scope, data, tools, and third-party components. Identify likely benefits and harms, the agent’s authority, and where oversight occurs. This mapping helps distinguish a low-impact assistant from an agent able to change records, send communications, or affect external systems.
Measure: evaluate behavior and exposure
Evaluate risks that matter in the actual context. For an agent using tools, practical testing can examine how it handles untrusted content, whether tool permissions match the task, and whether it pursues the intended objective. These are implementation examples, not a universal test suite prescribed by NIST; the appropriate evaluations depend on the system and its use.
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Manage: prioritize, respond, and update
Choose controls based on the risks identified, then monitor operation and revise them as conditions change. Prepare incident response and recovery procedures, change management, ways to appeal or override consequential decisions, and a plan for decommissioning the system. NIST’s AI RMF includes post-deployment monitoring and response mechanisms as part of risk management.
Constrain and observe actions
Limit access to what the task requires, require review for consequential actions, and retain records useful for understanding actions and outcomes. These are practical ways to apply the need to constrain and monitor agent access; they are not universal implementation requirements stated by NIST. Reassess permissions when the agent’s purpose, tools, or operating context changes.
What risks are unique to AI agents?
Some risks arise from how agents combine model behavior with tool access and autonomous action. Others are familiar cybersecurity weaknesses that become consequential when an agent can reach important systems.
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- Indirect prompt injection: Adversarial instructions embedded in data an agent encounters may influence its behavior, even when the user did not directly provide those instructions.
- Data poisoning: An insecure model may be affected by manipulated training or other data.
- Specification gaming or misaligned objectives: An agent may take harmful actions while pursuing an objective, even without an adversarial input.
- Conventional software vulnerabilities: Exploitable authentication or memory-management flaws can expose the agent’s connected systems or capabilities.
NIST’s 2026 announcement about its AI agent security request for information discusses these agent-specific and conventional risks. Its 2026 analysis of responses reports broad agreement among commenters that agents present novel security threats and that established cybersecurity practices remain relevant but need adaptation. That is a qualitative summary of commenters’ views, not a measured rate of incidents or proof that every agent has experienced a security event.
How do you secure an AI agent that can use tools?
Start with the boundary between the model and the software capabilities it can invoke. Treat each tool as a potential route to a consequential action, and evaluate what the agent can access and change in the specific deployment.
- Inventory tools, connected systems, data sources, and the actions each capability permits.
- Match access to the task rather than granting broad authority by default.
- Test how the agent responds to untrusted content and whether its actions stay aligned with the intended objective.
- Set review or approval points for actions with meaningful consequences.
- Monitor actions and outcomes, and maintain procedures for incidents, recovery, and changes to the system.
These are practical applications of lifecycle risk management and NIST’s stated interest in constraining and monitoring access; they should be tailored to the system rather than treated as a complete agent-security checklist.
How can organizations monitor and constrain AI agent access?
Make access and action limits explicit, then check that they remain appropriate as the system changes. A useful operating plan identifies who can change permissions, what actions require human review, which events are recorded, who reviews those records, and how access is suspended or restored after a problem.
Monitoring is not a substitute for limiting authority: records may help an organization understand what happened, but they do not prevent an agent from taking an action it was already permitted to take. Pair monitoring with task-appropriate permissions, oversight for consequential actions, and a response process that can stop or recover from unwanted behavior. NIST’s 2026 request for information specifically asks about interventions to constrain and monitor access, while leaving implementation choices to the deployment context.
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NIST AI RMF 1.0, released January 26, 2023, is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its four functions—Govern, Map, Measure, and Manage—provide an adaptable lifecycle structure for agent systems. NIST says the framework is being revised; it should not be presented as a finalized, agent-specific standard. The AI RMF page and companion Playbook explain the framework and suggest actions for achieving its outcomes. The Playbook is guidance to tailor, not a mandatory checklist.
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For organizations considering another guidance option, ISO/IEC 23894:2023 was published in February 2023 and gives organizations that develop, produce, deploy, or use AI systems guidance on risk management and integrating it into AI-related activities. ISO says the guidance can be customized to an organization and its context. Neither the cited NIST nor ISO material establishes that using a framework alone makes a deployment safe or legally compliant.
Choose guidance by fit, not by a presumed winner
When selecting or adapting an organizational approach, check whether it:
- Covers the lifecycle from design through deployment, monitoring, and retirement.
- Can be adapted to the organization’s use, resources, risk tolerance, and affected parties.
- Addresses tools, autonomous actions, untrusted inputs, and changing behavior.
- Supports testing, monitoring, escalation, recovery, and updates to controls.
- Makes risk ownership, human oversight, and limits on authority clear.
NIST AI RMF and ISO/IEC 23894 are guidance options, not evidence that any particular agent deployment is safe. The cited sources do not establish a universally superior framework.
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