Ownership should sit with the application and platform teams that understand runtime behaviour, while security and engineering governance set the acceptance criteria. Automated analysis can narrow the decision, but humans should still approve changes that alter APIs, compatibility, or code paths. Clear accountability matters most when the update touches critical services or shared libraries.
Why This Matters for Security Teams
Dependency update decisions are not just package-maintenance chores. A larger upgrade can change APIs, transitive dependencies, runtime behaviour, and failure modes, which means ownership has to sit with the teams that understand the application and the platform it runs on. Security can define the guardrails, but it cannot safely approve every code-path change from a distance. That division of responsibility is a core control pattern in NIST SP 800-53 Rev 5 Security and Privacy Controls.
This matters because dependency risk is often discovered too late. The Ultimate Guide to NHIs notes that 79% of organisations have experienced secrets leaks, with 77% of those incidents causing tangible damage, which is a reminder that software supply-chain changes often have identity and secrets consequences as well as code consequences. In practice, many security teams encounter upgrade fallout only after a shared library change has already broken authentication, altered permissions, or exposed a secret path in production rather than through intentional review.
How It Works in Practice
The practical model is shared decision-making with clear accountabilities. Automated analysis should first classify the upgrade: patch-level, minor version, major version, or a change that introduces new dependencies, removes methods, or alters security-relevant defaults. If the change stays within a low-risk boundary, platform teams can often approve it under pre-agreed policy. If it crosses compatibility or behavioural thresholds, application owners should make the final call after reviewing test results, runtime impact, and rollback options.
Security and governance teams should set the acceptance criteria, not substitute for technical ownership. A good workflow usually includes:
- Automated diffing for API changes, transitive dependency shifts, and license or vulnerability findings.
- Test evidence from unit, integration, and staging environments before approval.
- Explicit sign-off when the upgrade touches authentication, secrets handling, payment flows, or shared libraries.
- Rollback planning and version pinning when the blast radius is uncertain.
For identity-heavy systems, this is especially important because dependency updates can affect service accounts, token libraries, and secrets retrieval paths. NHIMG research on the LiteLLM PyPI package breach shows why package trust cannot be separated from runtime trust. The control objective is not to block upgrades, but to make sure the people closest to the workload own the decision while governance enforces evidence and traceability. These controls tend to break down when large monorepos or cross-team shared libraries leave no single team with full knowledge of downstream compatibility.
Common Variations and Edge Cases
Tighter approval controls often increase release overhead, so organisations have to balance delivery speed against the risk of introducing hidden behavioural changes. That tradeoff becomes sharper when automated tools recommend a larger upgrade than the original fix target, because a “safer” security patch may also bring compatibility debt.
Current guidance suggests three common exceptions. First, if the dependency is a shared platform component used across many services, platform engineering should own the technical decision and application teams should approve impact on their workloads. Second, if the upgrade changes cryptographic, identity, or secrets libraries, security review should be mandatory because the blast radius extends beyond the application itself. Third, if the repository has strong tests and deployment isolation, teams may pre-authorise certain upgrade ranges, but there is no universal standard for this yet.
The governing principle is simple: automation can recommend, but accountable owners must decide when the upgrade may alter behaviour, compatibility, or trust boundaries. When that boundary is unclear, defer to the team that can explain the runtime impact and accept the operational risk, not the team that only sees the scan result.
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, OWASP Agentic AI Top 10 and CSA MAESTRO 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 Non-Human Identity Top 10 | NHI-03 | Dependency updates can alter secrets and service identity handling. |
| OWASP Agentic AI Top 10 | A-04 | Automated analysis should not override accountable human approval. |
| CSA MAESTRO | TRI-2 | Shared accountability is needed when platform and app teams both absorb upgrade risk. |
| NIST AI RMF | Governance should define who accepts risk from model-driven automation outputs. | |
| NIST CSF 2.0 | PR.IP-1 | Changes should follow documented processes with evidence and approvals. |
Set decision rights and escalation paths for automated recommendations before they affect releases.
Related resources from NHI Mgmt Group
- Who should own the next steps after an MSP webinar update is released?
- Who should own decisions when unused cloud identities are disabled or removed?
- Who should own decisions about API governance when AI agents, partners, and cloud native services all depend on the same interfaces?
- Who should own context-aware security decisions across code, pipelines, cloud, and runtime?
