AI agents and unsanctioned tools blur the line between human and machine actions, often using the same credentials and data paths as employees. That makes conventional monitoring too narrow. Security teams need visibility into who used the tool, what data was touched, and whether the action matched policy, role, and expected behavior.
Why This Matters for Security Teams
AI agents and shadow ai break the assumptions behind traditional access and awareness controls because the “user” is no longer reliably human, and the workflow is no longer fully visible. A model may request data, a connected tool may execute actions, and the resulting activity can look like ordinary employee behaviour unless teams inspect context. That is why current guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 places strong emphasis on governance, traceability, and abuse-resistant design.
The practical risk is not just unauthorised access. It is misattributed access, where an agent operates under a user’s session, a service account, or a shared API key without a clear record of intent. Awareness controls also weaken because users may not realise they have introduced an unsanctioned AI workflow that copies sensitive data into an external prompt, browser plugin, or workflow connector. Once those paths exist, conventional monitoring often sees only destination systems and login events, not the decision chain that led there.
In practice, many security teams encounter shadow AI only after data has already been copied into an unapproved service or an agent has triggered a high-risk action that looked routine in logs.
How It Works in Practice
Effective control starts by treating AI agents as actors with bounded authority, not just applications. That means mapping which identities can invoke a model, which tools the model can reach, what data it can retrieve, and which actions require human approval. Security teams should align this with identity governance and telemetry so each request can be traced from the initiating user or system to the model call, tool invocation, and downstream side effect. The NIST Cybersecurity Framework 2.0 is useful here because it ties governance, asset visibility, and response into one operational view.
A practical control stack usually includes:
- approved model and tool inventories, including shadow AI discovery across browsers, endpoints, and SaaS connectors;
- policy checks before prompts, retrieval, or action execution;
- session-level logging that preserves the initiating identity, the agent identity, and the target resource;
- data classification rules that limit what can be sent to external models;
- approval gates for sensitive actions such as payment changes, code deployment, or privilege escalation.
For threat modeling, MITRE ATLAS adversarial AI threat matrix helps teams reason about prompt injection, tool abuse, and model manipulation, while the CSA MAESTRO agentic AI threat modeling framework is useful when an organisation needs to evaluate agent workflows, orchestration trust, and control points across multiple tools.
The control objective is simple: prove which identity asked for the action, which AI component transformed that request, and whether the final act matched policy and expected behaviour. These controls tend to break down in highly distributed SaaS environments because tool usage is fragmented across browser extensions, API tokens, and unsanctioned workflow automation.
Common Variations and Edge Cases
Tighter control over AI agents often increases user friction and operational overhead, requiring organisations to balance speed of adoption against traceability and data protection. That tradeoff is especially visible in engineering teams, customer support workflows, and knowledge work where employees will adopt shadow AI if approved tools are too slow or restrictive.
Best practice is evolving for agentic environments, and there is no universal standard for this yet. Some teams focus on network-level restrictions, while others prioritise application allowlisting or prompt-layer controls. In reality, those approaches are complementary. Network controls can reduce exposure, but they rarely explain why a model accessed a file or why an automated action was triggered. Application controls can add context, but they may miss personal devices, unmanaged browsers, or external plugins.
The most difficult edge case is shared or delegated access, where a human authorises an agent to act on their behalf. That can be legitimate, but it creates ambiguity for audit, incident response, and insider-risk investigations. In these cases, organisations need explicit delegation records, scoped tokens, and retention of prompt and action metadata. Shadow AI is also harder to contain when staff paste sensitive content into consumer tools that sit outside enterprise identity controls, which is why discovery and awareness must work together rather than as separate programmes. The OWASP Non-Human Identity Top 10 is especially relevant where agents rely on long-lived secrets, service identities, or poorly governed machine credentials.
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, MITRE ATLAS and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | AI governance and traceability are central to controlling agent-driven access. | |
| OWASP Agentic AI Top 10 | Agentic AI threats include prompt injection, tool abuse, and unsafe autonomy. | |
| MITRE ATLAS | ATLAS covers adversarial AI attacks that evade normal access monitoring. | |
| NIST CSF 2.0 | GV.AM, PR.AA, DE.CM | Asset visibility, access control, and continuous monitoring are directly challenged here. |
| OWASP Non-Human Identity Top 10 | Agents often depend on secrets and machine identities that are poorly governed. |
Use AI RMF governance to assign ownership, define risk tiers, and require traceability for each AI action.
