How should security teams make red team exercises more realistic?

They should anchor scope to observed attacker behaviour, not to whatever playbooks are easiest to demonstrate. That means weighting discovery, evasion, persistence, and exfiltration more heavily when those stages dominate real incidents, then using findings to test the controls that would have broken the attack chain earlier.

Why This Matters for Security Teams

Realistic red team exercise are useful only when they pressure the same parts of the environment that a capable adversary would target. If the exercise focuses on dramatic exploitation but skips reconnaissance, privilege escalation, lateral movement, or data access paths, the result is a confidence exercise rather than a security test. The goal is to validate whether monitoring, access controls, incident response, and recovery can withstand an attack chain that resembles current tradecraft.

That is why scoping should be informed by real incidents, threat intelligence, and control objectives, not by what is easiest to demo. The NIST Cybersecurity Framework 2.0 is useful here because it frames security work around governance, protection, detection, response, and recovery rather than isolated technical events. A realistic exercise should test how those functions perform under operational pressure, including whether defenders notice low-noise activity and whether containment steps can be executed quickly enough to matter.

Teams also need realism for a second reason: the wrong exercise can create blind spots. If the red team only attacks assets that are already heavily monitored, the organisation may miss weak identity boundaries, poor secret handling, or gaps between cloud and on-prem controls. In practice, many security teams encounter the failure only after an actual intruder has already blended in, rather than through intentional adversary emulation.

How It Works in Practice

A realistic red team design starts with evidence. Mature teams review recent intrusion reports, industry advisories, internal incidents, and control failures to identify the techniques most likely to matter in their environment. The aim is not to copy a public breach exactly, but to build an exercise around the behaviours that would succeed against this specific organisation.

• Map likely attacker paths from initial access to objectives such as credential theft, data exfiltration, or service disruption.
• Prefer stealthy steps like discovery, token abuse, and living-off-the-land activity when those are common in real incidents.
• Define detection goals in advance so success is measured by defender visibility, not by whether the red team can “win.”
• Include response validation, such as escalation workflows, containment decisions, evidence preservation, and recovery timing.

Operational realism also depends on constraints. The exercise should account for cloud control planes, remote work patterns, privileged access pathways, and the places where identity becomes the real perimeter. Where the scenario involves credentials or tokens, teams should evaluate whether PAM, just-in-time elevation, and secret hygiene would actually interrupt the chain. For broader adversary emulation guidance, the MITRE ATT&CK knowledge base remains a practical reference for mapping techniques to detections and response playbooks.

Best practice is evolving on how much infrastructure and business context to expose to the red team, especially in highly regulated environments. Current guidance suggests balancing realism with safety and scope governance: enough access to reveal control gaps, but not so much that the exercise becomes indistinguishable from uncontrolled activity. These controls tend to break down when the environment is highly segmented across cloud, SaaS, and legacy systems because attacker movement can traverse ownership boundaries faster than defenders can coordinate a response.

Common Variations and Edge Cases

Tighter realism often increases operational overhead, requiring organisations to balance fidelity against safety, time, and business disruption. That tradeoff is especially visible when exercises involve production systems, sensitive data, or executive communications, where an overly aggressive test can create unnecessary risk.

One common variation is the use of purple team collaboration. This can improve learning speed, but it also reduces the surprise factor, so it works best when the objective is control tuning rather than pure adversary emulation. Another edge case is the use of automation or AI-assisted tooling. Those capabilities can make exercises more realistic, but they also raise governance questions about safety controls, logging, and whether the test reflects current attacker behaviour or merely tool novelty.

For identity-heavy environments, realism should extend to how access is obtained and abused. If the organisation relies on federated identity, device trust, or non-human identities, the exercise should include those pathways rather than focusing only on workstation compromise. The MITRE ATT&CK framework helps here, but it does not replace local context about which identities, tokens, or service accounts actually matter. In some sectors, the NIST Cybersecurity Framework 2.0 is a better umbrella for deciding how exercise results map to governance and recovery priorities. There is no universal standard for “realism” yet, so the most defensible approach is to document the threat assumptions, test boundaries, and explain where the scenario intentionally differs from live attacker behaviour.

Related resources from NHI Mgmt Group

• How should security teams use red team and blue team exercises to improve attack-surface control?
• How should security teams red team AI agents that use tools and memory?
• Why do traditional red team exercises miss so many AI security issues?
• What breaks when AI security testing is done only in scheduled red team exercises?