Manual approval workflows usually fail when the volume and speed of change exceed human review capacity. Risky code can move too far before anyone detects it, and audit evidence becomes fragmented across teams and systems. A stronger model uses automated checks for policy violations, then reserves human review for exceptions and higher-risk changes that need judgment.
Why This Matters for Security Teams
Manual approval workflows create a false sense of control when SAP changes are frequent, cross-system, and time-sensitive. Review queues can become a bottleneck, while the actual risk sits in the gap between submission and approval. NIST guidance on continuous risk management in the NIST Cybersecurity Framework 2.0 makes clear that governance has to be operational, not just procedural. For SAP environments, that means change approval cannot be the only control.
The practical issue is that manual reviews rarely see the full blast radius. A harmless-seeming transport can carry credential exposure, privilege escalation, or unsafe integration logic that only becomes obvious after deployment. NHIMG research on the State of Secrets in AppSec shows how fragmented secret handling and delayed remediation weaken assurance, which is exactly the kind of blind spot manual change boards tend to inherit. In practice, many security teams encounter the failure only after a transport has already reached production and the audit trail is being reconstructed backwards.
How It Works in Practice
The stronger pattern is to shift from approval-first to policy-first change governance. Manual reviewers should not be the primary filter for every SAP change. Instead, automated checks should evaluate the change against defined controls before a human ever sees it. That includes code scanning, configuration drift detection, secret detection, privilege checks, and transport integrity validation. Human reviewers then focus on exceptions, not routine traffic.
In SAP estates, this is especially important because a single change can touch application code, authorization objects, RFC connections, background jobs, and interface credentials. A manual approver may understand process risk, but not always the technical consequences across those layers. Current guidance suggests using layered control points that are machine-enforced and time-bound, then preserving human judgment for ambiguous or high-impact cases. That is consistent with SAP breach lessons where delayed detection and incomplete visibility made response harder. It also aligns with the GitHub Action tj-actions Supply Chain Attack research, where speed and hidden dependencies created outsized impact.
- Use policy-as-code to block unsafe changes before approval, rather than after deployment.
- Require automated secret and credential checks on every transport, build, and deployment artifact.
- Route only exceptions, privileged changes, and boundary-crossing updates to human approvers.
- Log machine decisions and reviewer overrides in one evidence trail for auditability.
This model works best when SAP change tooling is integrated with CI/CD, IAM, and logging platforms so the control decision happens at request time. These controls tend to break down when transports bypass the pipeline, because then the organisation loses the ability to inspect the change before it reaches the target system.
Common Variations and Edge Cases
Tighter automated control often increases change friction, requiring organisations to balance delivery speed against governance depth. That tradeoff matters most in SAP landscapes with emergency fixes, regional support teams, or legacy transport paths that do not fit a modern pipeline. Best practice is evolving here: there is no universal standard for how much should be automated versus manually approved, but there is broad agreement that manual review alone is not sufficient.
Some changes still need human context, especially where business process interpretation, regulatory exception handling, or cross-functional sign-off is required. The problem is not human review itself, but making it the only gate. For example, a low-risk configuration tweak and a transport that changes privileged access should not travel through the same approval path. Where SAP systems are heavily customised, automated rule sets may need phased rollout and tuning to avoid blocking legitimate work while still catching high-risk deltas. NHIMG’s research on SAP SQL Anywhere Monitor Hardcoded Credentials shows why static credential exposure belongs in the same review scope as code risk. The operational edge case is simple: if the organisation cannot detect and classify the change before approval, the manual workflow becomes a record-keeping step instead of a control.
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 CSA MAESTRO address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Manual SAP change reviews often miss exposed or stale secrets in transports. |
| NIST CSF 2.0 | PR.AC-4 | Change approvals must enforce least privilege, not just document it. |
| NIST AI RMF | Governance should be operational and continuously monitored, not purely manual. | |
| NIST Zero Trust (SP 800-207) | Policy Enforcement Point | Policy decisions should occur at request time before a change is allowed through. |
| CSA MAESTRO | GOV-02 | Agentic control patterns apply when workflow automation decides what needs human review. |
Use AI RMF governance principles to make change oversight measurable, repeatable, and auditable.
Related resources from NHI Mgmt Group
- What breaks when organisations rely on manual workflows to manage SaaS identities?
- What breaks when healthcare teams rely on manual access reviews and role management?
- What breaks when identity governance processes rely too heavily on manual reviews and assessments?
- What breaks when financial institutions rely on manual access reviews instead of governed workflows?
