Accountability sits with the organisation, but operational ownership should be explicit. Compliance, security, legal, and business leaders need a shared approval process that records purpose, risk, and decision authority. If a tool touches regulated data or critical workflows, teams should be able to show who approved it, who reviews it, and what controls are in place.
Why This Matters for Security Teams
A high-risk SaaS or AI tool used without documented purpose or review is not just an approval lapse. It is an accountability gap that can leave security, compliance, legal, and business leaders arguing over ownership after the fact. NHI-related incidents routinely show how fast “temporary” access and unmanaged integrations become lasting exposure, as seen in NHIMG’s coverage of the Salesloft OAuth token breach and the BeyondTrust API key breach.
The risk is not limited to secrets leakage. An unreviewed tool may process regulated data, connect to production systems, or create autonomous access paths that no one can later explain to auditors or incident responders. That is why current guidance from the NIST Cybersecurity Framework 2.0 and NIST SP 800-53 Rev. 5 Security and Privacy Controls pushes organisations toward traceable governance, not informal approval chains. In practice, many security teams encounter this only after a tool has already touched sensitive data and no one can prove who accepted the risk.
How It Works in Practice
Accountability should be assigned at two levels: organisational ownership and operational approval. The organisation remains responsible for the risk, but a named business owner, security reviewer, and governance approver should be able to explain why the tool exists, what data it touches, and what controls are enforced. For NHI-heavy environments, that includes secrets handling, token scope, identity boundaries, and integration paths. The issue is often not whether a tool was “allowed” in a general sense, but whether the use case, access, and monitoring were reviewed before deployment.
Practical governance usually starts with a lightweight intake process that captures purpose, data classification, vendor dependencies, and AI or automation behavior if applicable. For example, an AI assistant that can query internal systems should be treated differently from a passive productivity app. Teams should record:
- the business purpose and sponsor;
- the data types and systems involved;
- the access granted, including API keys, OAuth grants, or service accounts;
- the reviewer who approved or rejected the request;
- the review cadence for revalidation or removal.
NHIMG research on The 2024 ESG Report: Managing Non-Human Identities shows how quickly NHI weaknesses become real incidents, which is why this review trail matters. In parallel, the Top 10 NHI Issues page reinforces that unmanaged non-human access is rarely a one-time event; it is usually a pattern of weak inventory, weak ownership, and weak rotation. These controls tend to break down when business teams can self-enable SaaS tools without security review because access is fast, distributed, and poorly inventoried.
Common Variations and Edge Cases
Tighter approval controls often increase friction, requiring organisations to balance speed of adoption against evidence of review. That tradeoff becomes especially sharp in SaaS-heavy environments, where teams expect near-immediate access and may bypass formal processes if the path looks slow.
There is no universal standard for every scenario, but current guidance suggests a stricter threshold when a tool can access regulated data, production systems, customer records, or other high-impact workflows. Low-risk tools may justify simplified review, while AI tools that can infer, generate, or route sensitive content should be escalated for deeper assessment. For autonomous or agentic tools, the question is not only who clicked approve, but whether the system can act beyond the original request. That is where shared accountability must include the sponsor, the technical owner, and the approver who accepted the residual risk.
One useful benchmark is whether the organisation could reconstruct the decision later from records alone. If the answer depends on memory, chat logs, or informal consent, the review process is too weak. Teams that manage this well treat approval as a lifecycle control, not a procurement checkbox. They also link governance to removal, so a tool that loses its purpose is decommissioned rather than left to persist with standing access.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-53 Rev 5 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 | Risk ownership and review records support governance for unapproved SaaS and AI tools. |
| NIST SP 800-53 Rev 5 | PM-30 | Tracks rules for managing and reviewing externally provided services and tools. |
| OWASP Non-Human Identity Top 10 | NHI-01 | Unreviewed tools often introduce unmanaged non-human identities and secret sprawl. |
| OWASP Agentic AI Top 10 | A01 | Autonomous tools can exceed intended use without purpose and review guardrails. |
| NIST AI RMF | AI RMF emphasizes governance, accountability, and documented risk decisions for AI use. |
Use AI RMF governance practices to record purpose, approval, and monitoring for each AI tool.
Related resources from NHI Mgmt Group
- Who is accountable when access review scoping decisions create audit gaps or miss high-risk roles?
- Who is accountable for controlling agent access when a high-impact tool is invoked?
- Who is accountable when AI-assisted design review misses a security issue before release?
- How should security teams use AI-assisted query building for access governance without weakening review quality?
