Synthetic Monitoring uses scripted checks to simulate user actions against an application or service. Those checks run from selected locations and intervals to verify availability, latency, and workflow behavior before real users are affected. It is especially useful for detecting broken paths, regional degradation, and regression after changes.
Expanded Definition
Synthetic monitoring is a proactive assurance method that uses scripted transactions to emulate real user journeys, then measures whether an application, API, or service responds as expected. Unlike passive telemetry, it creates controlled traffic on purpose, which makes it useful for availability checks, latency tracking, and workflow validation across regions and deployment stages.
In security and reliability programs, the term is often used alongside observability, but it is not the same thing. Observability explains what happened from live signals; synthetic monitoring tests whether a critical path still works before customers encounter failure. That distinction matters for services with authentication gates, payment flows, or identity-dependent journeys where a small configuration change can break the experience even when infrastructure looks healthy. The NIST Cybersecurity Framework 2.0 supports this kind of continuous verification by emphasizing ongoing monitoring and resilience outcomes.
The most common misapplication is treating synthetic checks as a full substitute for real-user telemetry, which occurs when teams rely on scripted success alone and miss production issues that only appear under authentic user behavior.
Examples and Use Cases
Implementing synthetic monitoring rigorously often introduces test maintenance overhead, requiring organisations to balance early detection of failure against the cost of keeping scripts accurate as interfaces change.
- Running a login and session renewal script from multiple regions to detect degraded access paths before users in one geography are affected.
- Testing a checkout or payment workflow after each release to catch broken redirects, expired certificates, or API dependency failures.
- Validating a public API endpoint at short intervals to identify latency spikes, authentication errors, or routing issues that uptime probes would miss.
- Checking internal service chains in a zero trust environment to confirm that identity, policy, and network controls still allow the intended transaction path.
- Monitoring a customer support portal or SSO flow after a configuration change to see whether federation, token exchange, or MFA prompts still complete successfully.
For teams building application resilience, synthetic checks are often paired with guidance from the NIST Cybersecurity Framework 2.0 so that failure detection is tied to operational response rather than just alert generation.
Why It Matters for Security Teams
Synthetic monitoring matters because many security-relevant outages begin as functional failures, not obvious attacks. A bad deployment, certificate problem, identity provider outage, or policy error can block access just as effectively as malicious activity, and the first signal is often a failed synthetic transaction. That makes the practice valuable to security teams that support service continuity, incident triage, and identity-heavy application paths.
The identity connection is especially important in environments where users, service accounts, and agents depend on authentication workflows. When synthetic checks include SSO, MFA, token exchange, or API authorization paths, they can reveal whether identity controls still behave as designed after a change. This is useful for NHI and agentic AI operations too, because a service or agent may appear healthy while its secrets, tokens, or delegated access have silently broken.
Teams should align the alerting model with response ownership, because a failed synthetic check is only useful if it routes to the group that can fix the dependency. Organisations typically encounter the true value of synthetic monitoring only after a production workflow fails at scale, at which point the term becomes operationally unavoidable to address.
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, NIST Zero Trust (SP 800-207), NIST SP 800-63 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM | Synthetic monitoring is a continuous monitoring activity that supports detection of service degradation. |
| NIST Zero Trust (SP 800-207) | Zero Trust depends on verifying access paths, including identity and policy enforcement during runtime. | |
| NIST SP 800-63 | Digital identity assurance depends on reliable authenticator and federation flows that synthetic tests can validate. | |
| OWASP Non-Human Identity Top 10 | NHI systems rely on tokens, secrets, and delegation paths that synthetic checks can help verify. | |
| NIST AI RMF | AI systems need ongoing monitoring to validate behavior and detect failures in operational context. |
Include login, MFA, and federation journeys in synthetic tests to confirm identity flows still complete correctly.
