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How Security Leaders Should Prepare for Real-World AI Agent Risks

Secure AI agents according to their data access, tools, identities, and authority. Learn which risks to assess and how to adapt established security controls.
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Security leaders should secure AI agents according to the access and authority they actually have—not assume that dramatic autonomous attacks are common or imminent. Start by inventorying agents, data, tools, identities, and permitted actions; then apply familiar security controls adapted to systems that can act, persist, and use delegated authority.

What makes an AI agent a security concern?

An agent combines model behavior with data, software tools, identities, and permissions. That combination can turn an unsafe or manipulated model output into an action against a real system. NIST’s Center for AI Standards and Innovation describes agents as systems capable of planning and taking autonomous actions that affect real-world systems or environments. Its January 2026 request for information focused on risks created when model outputs are combined with software functionality.

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The relevant attack surface includes familiar weaknesses—such as excessive privileges or insecure integrations—as well as risks tied to how agents interpret inputs, retain information, and choose actions. The practical question is not whether an agent sounds autonomous; it is what it can reach and what it can do.

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Which threats should a security review consider?

Assess plausible failure paths without treating their existence as evidence that they are prevalent. NIST and OWASP materials identify several areas to examine:

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  • Indirect prompt injection: untrusted content, such as a document or webpage, may influence an agent’s behavior when it is processed as part of a task.
  • Compromised or insecure models: NIST includes models affected by data poisoning among the risks to consider.
  • Tool misuse or exploitation: an agent may invoke a connected application or tool in an unsafe way, or an integration may expose a weakness.
  • Identity and privilege abuse: an agent’s credentials or delegated authority may allow actions beyond what its task requires.
  • Harmful actions without an adversarial input: specification gaming or misaligned objectives can produce damaging behavior even when nobody is actively attacking the system.

OWASP’s Agentic AI Threats and Mitigations guide and Threats Navigator organize assessment areas across reasoning, memory, tools, identity, human oversight, and interactions among agents. These are useful dimensions for threat modeling, not measurements of how often each failure occurs. OWASP’s 2026 Top 10 for Agentic Applications likewise highlights behavior hijacking, tool misuse, and identity or privilege abuse as practitioner concerns, rather than providing attack-frequency statistics.

How should leaders compare agent deployments?

Use the same review rubric across deployments so that decisions reflect the agent’s actual exposure and authority, rather than its label or the vividness of a hypothetical attack.

Review dimension Questions to answer
Data sensitivity and reach What information can the agent read, change, or disclose, and how sensitive is it?
External and untrusted content Can it process webpages, messages, documents, or other content that may contain adversarial instructions?
Tools and application permissions Which tools and applications can it invoke, and what can those integrations do?
Identity and privilege Which identity does it use, who owns that identity, and how narrowly is its authority scoped?
Persistence and memory Does it retain state or memory across tasks, and what can influence that retained information?
Delegation and agent interaction Can it delegate work or pass instructions, data, or authority to other agents?
Impact and reversibility Could an action materially affect people or systems, and can it be undone?
Oversight and observability Are actions logged, auditable, and subject to human approval where appropriate?

An agent that only drafts text for review presents a different control problem from one that can change records, send messages, or trigger transactions. The rubric helps identify that difference without assuming either deployment is safe or dangerous solely because it is called an agent.

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What controls should a security program put in place?

NIST’s May 2026 summary of responses reports broad agreement that fundamental cybersecurity practices remain relevant but require adaptation for agent security. A practical program can extend existing controls in several ways:

  • Inventory agents and integrations. Record what agents are deployed, what purpose they serve, which tools and data they can access, and who is accountable for them.
  • Restrict identity and permissions. Give each agent only the identity and authority needed for its task. Review delegated access, prevent unnecessary privilege, and make ownership clear.
  • Test adversarial inputs and tool use. Exercise how the agent handles untrusted content, attempts to redirect its task, and calls connected tools. Include plausible harmful behavior even when there is no attacker.
  • Monitor actions, not just outputs. Keep records that let the organization understand which identity acted, which tool was invoked, and what changed. Review activity against the agent’s intended purpose.
  • Add human review at consequential points. Require approval when an action could materially affect people or systems, especially where the action is difficult to reverse.

NIST’s NCCoE February 2026 concept paper on identity and authority of software agents explored identification, authorization, auditing, non-repudiation, and prompt-injection controls. Those themes make identity and accountability central parts of agent security, rather than an afterthought to model testing.

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How should leaders treat dramatic attack scenarios?

Use them as scenarios to assess, not as evidence of likelihood. The cited NIST and OWASP material identifies risk areas and control themes; it does not establish a comparative likelihood ranking between sensational autonomous attacks and conventional security threats, nor does it provide a sourced rate of agent-specific attacks or organizational deployment prevalence. A threat model should therefore distinguish what is technically possible from what is observed and from what matters for a particular deployment.

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OWASP’s 2026 Top 10 drew input from more than 100 security researchers, practitioners, user organizations, and technology providers, according to its announcement. That contributor count indicates broad practitioner input; it is not a measure of attack frequency or adoption. OWASP’s Q2 2026 security solutions landscape maps tools to agent-security tasks and lifecycle stages and is updated quarterly. It can help frame a later tool assessment, but selection should depend on the organization’s architecture and the current edition of the landscape.

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What is the right planning priority?

Prioritize deployments by authority, exposure, and potential impact. First establish what exists and what each agent can reach. Then narrow access, test likely failure paths, monitor actions, and place human approval where consequences warrant it. This approach addresses real control gaps while avoiding the unsupported claim that a spectacular attack is widespread or imminent.

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