How should security teams design cyberattack simulations for environments that include both employees and AI agents?

Security teams should design simulations that reflect the actual attack surface, including human users, service workflows, and AI agents with access to systems or data. The goal is to test the full response chain, not just click rates. Use realistic scenarios, correlate outcomes with identity and access signals, and measure whether detection, escalation, and containment work under pressure.

Why This Matters for Security Teams

Attack simulations now need to cover more than employee phishing or endpoint compromise. When AI agents can read messages, call tools, and trigger workflows, they become part of the operational attack surface and can be targeted through prompt injection, malicious instructions, poisoned inputs, or credential abuse. That changes what “success” means: not just whether a user clicks, but whether a simulation can expose unsafe delegation, weak approval logic, and brittle monitoring across human and machine actions.

For security teams, the value is in validating whether identity, access, logging, and response controls still hold when actions are taken by a person, a service account, or an autonomous agent. Good simulations should test escalation paths, tool permissions, and containment decisions in a way that reflects real adversary behavior. The MITRE ATLAS adversarial AI threat matrix is useful here because it helps teams think about AI-specific attack paths alongside conventional enterprise techniques.

In practice, many security teams discover weak agent governance only after an AI system has already been abused to move faster than their monitoring can interpret.

How It Works in Practice

Effective simulations begin with a complete map of participants: employees, contractors, service accounts, AI agents, and the systems or data each one can reach. Scenarios should be built around realistic adversary goals such as stealing secrets, coercing an agent into unsafe tool use, or using a compromised employee account to manipulate an AI workflow. The point is to observe how the environment behaves when trust is stressed, not to create a theatrical test.

Security teams should design scenarios that blend social engineering, identity abuse, and AI misuse. A strong exercise might combine a phishing email, a malicious document, and an agent that has access to tickets, chat, or code repositories. Teams should then track whether detection systems notice the initial access, whether access controls block lateral movement, and whether response teams can distinguish human intent from agent action. Current guidance suggests using both OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework to shape threat models, while correlating results with identity telemetry, approval logs, and application traces.

• Define the agent’s tool permissions and data boundaries before the exercise.
• Include identity events such as MFA prompts, token use, and privilege elevation.
• Test whether analysts can attribute actions to a person, script, or AI agent.
• Measure whether containment works without breaking legitimate business workflows.
• Validate that logging captures prompts, tool calls, and downstream effects.

These controls tend to break down in highly automated environments where agents can chain tools across multiple systems faster than logging, triage, and approval workflows can keep up.

Common Variations and Edge Cases

Tighter simulation scope often increases operational overhead, requiring organisations to balance realism against the risk of disrupting production workflows. That tradeoff becomes sharper when AI agents are embedded in customer support, software delivery, or financial operations, because test actions can quickly affect real users or real transactions.

There is no universal standard for this yet, but best practice is evolving toward tiered simulations: low-risk tabletop exercises for governance, controlled red-team scenarios for agentic workflows, and limited live-fire testing for high-value paths. Some environments may need extra caution where agents can write code, approve purchases, or access regulated data. In those cases, use pre-approved guardrails and strong rollback procedures, and treat simulation outputs as evidence of control maturity rather than proof of safety. The Anthropic report on the first AI-orchestrated cyber espionage campaign is a reminder that adversaries already experiment with agentic workflows, while CISA cyber threat advisories remain useful for grounding scenarios in current attacker tradecraft.

Where an organisation lacks clear ownership for AI agents, simulations often fail because responders cannot tell who is accountable for the action or who has authority to shut it down.

Related resources from NHI Mgmt Group

• How should security teams design integration layers for AI agents in real-time environments?
• How should security teams authenticate AI agents in enterprise environments?
• How should security teams govern browser-based AI agents in SaaS environments?
• How should security teams handle tool discovery for AI agents in MCP environments?