Synthetic data is information generated by a simulation or model rather than collected directly from the real world. It is useful for training and testing, but it still carries governance risk because it can reveal system behaviour, operational patterns, or business logic when broadly accessible.
Expanded Definition
Synthetic data is data generated by a simulation, rules engine, or model rather than collected directly from production systems or the real world. In NHI and agentic AI programs, it is used to test workflows, validate controls, and train models without exposing live customer records, credentials, or operational telemetry. That makes it attractive for privacy-preserving development, but it is not automatically low risk. Synthetic data can still encode sensitive system behavior, especially when it is generated from real prompts, logs, token usage patterns, or access traces.
Definitions vary across vendors on whether synthetic data must preserve statistical properties, utility for a task, or both. NHI Management Group treats the term pragmatically: if the output can be used to reproduce business logic, identity flows, or control weaknesses, it deserves governance even if no real record appears inside it. The most useful comparison is with anonymized or masked data, which is derived from real records, while synthetic data is produced to imitate them. For standards context, the NIST Cybersecurity Framework 2.0 reinforces that data handling must be governed across the full lifecycle, not only at collection.
The most common misapplication is treating synthetic data as inherently non-sensitive, which occurs when teams publish it broadly after generation without assessing what operational patterns it still reveals.
Examples and Use Cases
Implementing synthetic data rigorously often introduces a realism tradeoff, requiring organisations to weigh model utility and testing fidelity against the risk of leakage or overfitting to production behavior.
- Development teams generate fake API call logs to test detection rules without exposing live service-account activity, while still preserving enough structure to validate alert logic.
- Security engineers build synthetic access datasets to simulate privilege creep and review how Zero Standing Privilege controls would behave under realistic assignment patterns.
- Data science teams use synthetic customer interaction records to train an agentic model, then verify that the output does not expose hidden prompt patterns or internal business rules.
- QA teams create synthetic secrets and token lifecycles to exercise rotation workflows and failure handling before touching production systems.
- Governance teams compare synthetic telemetry against real operational traces to spot whether an agent is retaining too much context or reconstructing sensitive workflows.
These examples are especially relevant when testing service accounts, API keys, and automation flows described in Ultimate Guide to NHIs — Key Research and Survey Results, because synthetic datasets often mimic the very patterns attackers seek. For implementation guidance, the identity and access boundaries in NIST Cybersecurity Framework 2.0 are useful for deciding who can generate, approve, and publish synthetic outputs.
Why It Matters in NHI Security
Synthetic data matters because NHI programs often rely on it to test controls without touching production, yet the generated output can still expose privilege structure, rotation cadence, workflow dependencies, and vendor integration patterns. That is a real security issue when synthetic datasets are reused across engineering, analytics, and partner environments. NHI Management Group research shows that 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, and synthetic data workflows can accidentally replicate those same risky patterns if they are built from ungoverned production traces.
It also intersects with visibility. When only 5.7% of organisations have full visibility into service accounts, synthetic datasets can become the only safe way to observe identity behavior at scale, but only if the generation process is tightly controlled and access is limited. The lesson is not to avoid synthetic data, but to classify it by the sensitivity of what it reveals, not by whether it was machine-generated. The most important control is to treat synthetic outputs as governed artefacts with explicit retention, access, and review rules, especially when they reflect NHI activity or agent decision paths.
Organisations typically encounter synthetic data as a governance problem only after a test dataset, prompt archive, or analytics export reveals how their automation really works, 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 Agentic AI 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 | PR.DS | Synthetic data is a governed data asset that must be protected through its lifecycle. |
| NIST AI RMF | Synthetic data supports AI testing but can still leak sensitive system behavior. | |
| OWASP Agentic AI Top 10 | Agentic AI systems may expose prompts, tools, or workflow logic through synthetic outputs. |
Classify, store, and share synthetic datasets under explicit data security and retention controls.
