Who is accountable for making IRM work when organisations adopt a maturity model?

Accountability sits with risk, compliance, security, and business leaders together, because IRM only works when governance is shared across functions. Leaders must define ownership, metrics, and escalation paths, then sustain them over time. A maturity model helps clarify who manages controls, who reviews evidence, and who turns insights into strategic decisions.

Why This Matters for Security Teams

Accountability is the difference between a maturity model that improves decision-making and one that becomes a reporting exercise. In IRM, the risk of unclear ownership is not simply missed paperwork; it is duplicated controls, gaps in escalation, and evidence that looks complete while operational risk keeps growing. The control environment should map to business impact, not just security tasks, and that requires explicit ownership across risk, compliance, security, and business leadership. NIST guidance on control baselines in NIST SP 800-53 Rev 5 Security and Privacy Controls reinforces that controls only work when responsibilities are defined, monitored, and maintained.

The hardest part is that maturity models can create the illusion of shared governance without naming a single accountable decision-maker. That usually shows up when one team owns the framework, another owns the evidence, and business leaders treat risk acceptance as someone else’s job. In practice, many security teams encounter accountability failures only after a control gap, audit exception, or incident exposes who was not actually owning the decision.

How It Works in Practice

In a mature IRM operating model, accountability is usually separated into ownership of the risk, ownership of the control, and ownership of the decision. That distinction matters because a maturity model measures whether processes are repeatable and effective, but it does not automatically assign authority to accept, transfer, mitigate, or monitor risk. The strongest programmes define who is responsible for each stage of the lifecycle and make that explicit in governance forums, policies, and exception workflows.

A practical model normally includes:

• Business leaders who own the risk outcome and approve risk acceptance where appropriate.
• Risk and compliance teams who define criteria, test evidence, and challenge weak controls.
• Security teams who implement technical safeguards, monitor control effectiveness, and support remediation.
• Internal audit or independent review functions that validate whether the model is working as intended.

This aligns well with the control structure in NIST SP 800-53 Rev 5 Security and Privacy Controls, where control assignment, assessment, and continuous monitoring all depend on clear responsibility. It also fits the broader governance approach in the NIST AI Risk Management Framework, which emphasises governance, mapping, and management of risk across the lifecycle. For organisations using agentic AI or automation in IRM workflows, accountability should also cover who can change rules, tune thresholds, and override decisions, because those actions can alter exposure as much as any traditional system change.

Operationally, the best practice is to connect maturity scoring to named owners, decision logs, and remediation deadlines. That prevents the common failure mode where a dashboard shows improving maturity while no one is accountable for unresolved findings. These controls tend to break down when operating models span many business units and shared-service teams because ownership becomes fragmented and exception handling slows to a standstill.

Common Variations and Edge Cases

Tighter accountability often increases coordination overhead, requiring organisations to balance clear ownership against the speed of day-to-day decisions. That tradeoff becomes visible in decentralised enterprises, regulated sectors, and fast-moving technology teams where one global framework must fit multiple operating models.

There is no universal standard for exactly how accountability should be layered in every IRM maturity model. Some organisations use a three lines model, others use product-aligned ownership, and some merge risk and compliance functions in smaller environments. Current guidance suggests the structure matters less than whether it is auditable, repeatable, and understood by the people who must act on it.

The edge cases are usually where accountability crosses boundaries. For example, when third parties manage controls, when AI systems trigger risk decisions, or when control evidence is generated automatically, the accountable party still remains the organisation that owns the risk outcome. The tool or vendor may support the process, but it cannot hold governance responsibility. NIST CSF 2.0 is useful here because it frames governance as an organisation-wide function rather than a narrow security task, helping teams clarify who decides, who monitors, and who escalates when the maturity model reveals a weakness.

In high-regulation environments, this becomes even more important because regulators expect named ownership, documented oversight, and consistent response to exceptions. In practice, maturity models fail fastest when leadership expects the framework to create accountability on its own instead of assigning it before the first assessment begins.

Related resources from NHI Mgmt Group

• How do organisations operationalise NHI ownership at scale?
• When should organisations treat an NHI as a high-priority risk?
• How can organisations reduce the blast radius of compromised agent identities?
• Should organisations prioritise external exposure or internal credential governance first?