A strong maturity programme starts by measuring what actually drives risk: flaw prevalence, fix capacity, fix speed, debt prevalence, and open source debt. Security teams should use those metrics together, not in isolation, then create feedback loops that help engineers find, prioritise, and fix issues earlier in the SDLC. The goal is continuous risk reduction, not merely more scanning.
Build the programme around risk signals, not activity counts
A maturity programme only reduces risk when it measures the conditions that create exposure, then uses those signals to change engineering behaviour. Backlog volume can still be useful for capacity planning, but it should never be treated as the primary success metric because it does not distinguish low-value noise from issues that materially increase attack surface, exploitability, or recovery cost.
The most useful metrics are the ones that connect security findings to delivery reality: flaw prevalence shows how much insecure code is still entering the system, fix capacity shows whether teams can absorb and clear work, fix speed shows whether exposure is shrinking quickly enough, debt prevalence shows how much accepted weakness is accumulating, and open source debt shows how much risk is being inherited from dependencies. Those measurements only matter if they are reviewed together, because a fast backlog burn-down can still hide a rising risk profile if new defects arrive faster than teams can remediate them.
- OWASP SAMM is the clearest external maturity model for turning security improvement into measurable engineering outcomes.
- NIST Cybersecurity Framework 2.0 is useful when the programme needs a governance layer that ties measurement to manage, identify, protect, detect, respond, and recover outcomes.
- NHI Mgmt Group’s Ultimate Guide to Non-Human Identities provides a useful reminder that debt is not only code-based, because access and secrets sprawl can create the same persistent exposure problem.
Make the feedback loop change earlier decisions in the SDLC
The practical test for maturity is whether issues are found and fixed earlier, with less rework and lower blast radius. That means the programme should not just report on defects at the end of the pipeline; it should help engineers prioritise higher-risk work before release, connect patterns back to recurring root causes, and identify where controls are failing to prevent repeat findings.
That feedback loop is what turns a maturity dashboard into a risk-reduction system. If recurring issues are concentrated in a few services, libraries, or teams, the programme should surface the pattern and push preventive fixes into design, code review, dependency management, and release gating. If fix speed is improving but defect prevalence is not, the signal is usually that teams are clearing old work while the upstream control environment remains unchanged.
- SLSA helps teams reduce supply-chain risk by strengthening build provenance and integrity controls.
- OWASP API Security Top 10 is relevant when the maturity programme needs to prioritise recurring application-risk classes that are often missed by generic scanning.
- Nx Package Attack, 2,300+ Credentials Leaked is a useful internal example of how dependency issues can become direct exposure, not just hygiene problems.
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 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | Non-Human Identity Top 10 | Open source debt and secrets sprawl echo NHI exposure patterns. |
| Recommendation — Use NHI controls to reduce secret sprawl, overprivilege, and rotation gaps. | ||
| OWASP Agentic AI Top 10 | Agentic AI Top 10 | No direct agentic AI subject appears in the question. |
| Recommendation — Omit from the programme unless agent autonomy becomes a material risk driver. | ||
| NIST CSF 2.0 | GV.OC-01 — Organizational Context | The programme must align metrics with the organisation’s actual risk objectives. |
| Recommendation — Define maturity metrics that map to the organisation’s risk and delivery context. | ||
| CIS Controls v8 | 16 — Application Software Security | The question is about improving software security outcomes through measurable controls. |
| Recommendation — Track application-security outcomes that reduce exploitable weakness, not just backlog counts. | ||
Practitioner Guidance
What to prioritise: Build the programme around a small set of risk-linked measures, then require every dashboard review to answer one question: is exposure falling, or are we just moving tickets around? If a metric cannot change an engineering decision, it is usually a reporting metric, not a maturity metric.
What to verify: Check that the programme can show three things at once: where defects enter, how quickly they are removed, and whether the same weakness keeps returning. That combination tells you whether the organisation is improving control quality or merely increasing remediation throughput.
Practitioner takeaway: The right maturity programme rewards earlier risk removal, not higher scanner activity, so the decisive measure is whether the organisation is shrinking exposure faster than it is creating it.
Related resources from NHI Mgmt Group
- How should security teams build a security assessment programme that actually reduces software and infrastructure risk?
- How should organisations build a practical access management programme that reduces everyday security risk?
- How should security teams build a third-party risk programme that actually reduces identity risk?
- How should security teams build a patch compliance programme that actually reduces risk?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
