Copilots can still perform actions the user did not explicitly anticipate, especially when they have broad connectors, inherited permissions, or weak guardrails. User intent does not eliminate prompt injection, over-permissioned access, or unsafe data movement. The main risk is not the assistant itself, but the trust boundary between user, model, and downstream systems.
Why This Matters for Security Teams
Copilots are risky because they collapse the distance between a user request and a machine action. Once a copilot can search mailboxes, call APIs, move files, or create tickets, it becomes part of the trust path, not just a chat interface. That means the real control problem is whether the downstream permissions, connectors, and data flows are safe when the model is asked to act on behalf of a person. NIST’s Cybersecurity Framework 2.0 is useful here because it reinforces governance around identity, access, and data handling, but copilots introduce a faster, less predictable execution layer.
NHIMG research on the CoPhish OAuth Token Theft via Copilot Studio shows how quickly trust in an assistant can turn into token exposure when the surrounding permissions are loose. The same pattern appears in broader NHI incidents: the State of Non-Human Identity Security report notes that 85% of organisations lack full visibility into third-party vendors connected via OAuth apps. In practice, many security teams discover copilot exposure only after an OAuth chain, connector abuse, or data exfiltration path has already been exercised rather than through intentional design review.
How It Works in Practice
User intent is necessary, but it is not a sufficient security boundary. A copilot may be responding to a legitimate request while still being able to reach systems the user never meant to touch. The security question is not, “Did the user want help?” but “What can the system legally do in response?” That is why copilots should be treated as agent-like workloads with bounded execution authority, not as passive UI features.
In practice, the safest pattern is to separate identity, intent, and authorization:
- Use workload identity for the copilot runtime so actions are traceable to the service, not just the person who prompted it.
- Issue just-in-time, short-lived credentials for each task instead of inheriting broad, standing access.
- Evaluate policy at request time, based on context such as resource sensitivity, data classification, and action type.
- Restrict connectors so the copilot can only call approved systems with narrowly scoped permissions.
- Log the full action chain, including prompt, tool call, token use, and downstream effect.
This is where guidance from OWASP NHI Top 10 becomes practical: the risk is not only credential theft, but also over-privileged execution paths that a user did not explicitly intend. Agentic governance frameworks such as Ultimate Guide to NHIs, Key Challenges and Risks map cleanly to this model because they emphasise runtime control, not just identity issuance. These controls tend to break down when copilots are deployed inside legacy SaaS environments where connector permissions cannot be segmented per action and auditing is limited to coarse admin logs.
Common Variations and Edge Cases
Tighter copilot controls often increase operational friction, requiring organisations to balance user productivity against approval latency and connector complexity. That tradeoff is real, especially in business units that expect the assistant to “just work” across email, files, chat, and CRM.
Best practice is evolving, but current guidance suggests a few important distinctions. A read-only copilot with limited retrieval risk is very different from one that can approve spending, delete records, or send messages externally. Likewise, a copilot operating on a single tenant with tightly governed data is not equivalent to one with cross-domain SaaS connectors and delegated OAuth grants. For the latter, static role-based access is usually too blunt, because the agent can combine tools in ways the original role model never anticipated.
Security teams should also watch for edge cases where “user intent” is easy to spoof. Prompt injection, malicious content in retrieved documents, and indirect tool manipulation can all cause a copilot to act consistently with the user’s stated goal while still violating policy. This is why Top 10 NHI Issues remains relevant: governance gaps are often exposed at the seams between identity, permissions, and automation, not in the copilot interface itself. There is no universal standard for this yet, so organisations should treat high-risk copilots as controlled automation until their trust boundaries are proven.
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, CSA MAESTRO 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 |
|---|---|---|
| OWASP Agentic AI Top 10 | LLM-03 | Covers tool abuse and agent actions that exceed user intent. |
| CSA MAESTRO | GOV-04 | Addresses governance for autonomous and semi-autonomous agent workflows. |
| NIST AI RMF | GOVERN | Requires accountable oversight for AI systems that take actions. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Relevant to over-permissioned tokens and weak rotation in copilot connectors. |
| NIST CSF 2.0 | PR.AC-4 | Maps to managing access rights for the copilot and its downstream systems. |
Use short-lived tokens, rotate secrets, and remove standing access from connector accounts.
Related resources from NHI Mgmt Group
- Why do user provisioning failures create security risk even when onboarding is fast?
- Why do AI models with tool access create security risk even when they are not autonomous?
- Why do AI security tools create governance risk even when they only generate findings?
- Why do AI coding agents create security risk even when they use the same model?
