Custom Rules are user-authored detection conditions built from runtime evidence observed in production. They let security teams encode environment-specific logic around functions, libraries, call chains, and process behavior so detections reflect actual application execution rather than generic assumptions.
Expanded Definition
Custom Rules are tailored detection statements that security teams write to match how a specific environment actually behaves in production. Unlike generic signatures or vendor-tuned policies, these rules are built from observed runtime evidence, such as function calls, library usage, process lineage, argument patterns, and execution sequences. That makes them especially useful where application logic is unique, where default detections are too noisy, or where defenders need to capture a very specific abuse path. In practice, the term sits at the intersection of observability, detection engineering, and application security, with growing relevance in cloud and agentic systems where behavior is more important than static asset labels.
The concept is not fully standardized across vendors. Definitions vary across tools, and some platforms use “custom rule” to mean a condition, a query, a policy exception, or a workflow trigger. For glossary purposes, NHI Management Group treats the term narrowly: a custom rule is a user-authored condition derived from runtime evidence, not a broad product setting. That distinction matters because detection quality depends on the evidence source, the scope of the condition, and how tightly the rule maps to real execution paths. Guidance from the NIST Cybersecurity Framework 2.0 supports this risk-based approach to security monitoring.
The most common misapplication is treating a generic vendor policy as a custom rule, which occurs when teams copy predefined logic without validating it against their own application runtime and process behavior.
Examples and Use Cases
Implementing custom rules rigorously often introduces maintenance overhead, requiring organisations to balance detection precision against the time needed to test, tune, and update conditions as software changes.
- A security team writes a rule to alert when a specific service binary loads an unusual library chain that has never been observed during normal production execution.
- Detection engineers create a rule to flag process behavior where an agent launches shell commands after a particular API call sequence, indicating possible abuse of execution authority.
- Application security teams define a rule for suspicious access to secrets endpoints when the request originates from a nonstandard runtime path rather than an approved service account.
- Cloud defenders use a custom rule to identify abnormal parent-child process relationships in container workloads, then compare results to baseline behavior collected from real traffic.
- Teams maintaining agentic systems build rules that watch for unexpected tool access patterns or function chaining that deviates from approved operational workflows, a use case increasingly discussed in sources such as the NIST Cybersecurity Framework 2.0.
These use cases are strongest when the rule is anchored in evidence from the target environment, not inferred from documentation alone. In mature programs, custom rules are often paired with alert triage logic so teams can separate true anomalies from predictable edge cases.
Why It Matters for Security Teams
Custom rules matter because they turn local operational knowledge into actionable detection. Generic controls often miss application-specific abuse, especially in environments with bespoke services, nonstandard libraries, ephemeral workloads, or autonomous software entities. Well-constructed rules can reduce false positives, improve signal quality, and expose behavior that would otherwise blend into normal traffic. They also create a practical bridge between engineering and security, since the people who understand the runtime are often the people best placed to define what “normal” should look like. That is why custom rules are increasingly relevant in NHI and agentic AI contexts, where execution authority, tool use, and process behavior must be monitored with precision.
The risk is that poorly governed rules become brittle, inconsistent, or impossible to maintain. When teams overfit on a narrow event pattern, they can miss variants of the same attack path or create alert floods after routine software changes. Strong practice treats custom rules as living controls, with versioning, testing, and review tied to production telemetry and change management. Organisations typically encounter the operational cost of weak rule design only after an incident or a major platform change, at which point custom rules become unavoidable to restore detection fidelity.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM | CSF monitoring outcomes align with custom rules that detect abnormal runtime behavior. |
| NIST AI RMF | AI RMF governs measurement and monitoring of AI behavior, which custom rules can operationalize. | |
| OWASP Non-Human Identity Top 10 | NHI guidance emphasizes runtime-aware controls for non-human identities and their tool use. |
Use custom rules to support continuous monitoring and tune detections against observed production behavior.
