What Good Looks Like: Defining SecOps Maturity in the Age of AI

Maturity models have always had a complicated relationship with operational reality. At their best, they give security leaders a shared language for describing where they are and a credible map of where they need to go. At their worst, they become a compliance checklist that mistakes the presence of tools for the presence of capability. In 2026, with AI now embedded across the detection and response landscape, the gap between the two has never been wider.

The traditional maturity conversation, built around frameworks like the SOC-CMM or adapted versions of the CMMI, was designed for a world of manual analyst workflows, perimeter-based thinking, and relatively predictable threat actor tooling. That world no longer exists. Adversaries are using AI to accelerate reconnaissance, generate convincing phishing content at scale, and adapt malware faster than signature-based detection can follow. The security operations function that is not actively incorporating AI into its own workflows is not standing still. It is falling behind.

This piece offers a practical framework for CISOs seeking to benchmark their SecOps function against a definition of maturity that reflects the current threat and technology environment. It is not a vendor checklist. It is a strategic lens.

Why the Old Models Need Updating

Classic SecOps maturity frameworks typically measured progress along axes like process documentation, technology coverage, and analyst capability. A Level 1 organisation was reactive and largely undocumented. A Level 5 organisation had optimised, continuously improving processes with full visibility across the estate.

The problem is not that these dimensions are wrong. It is that they are incomplete. They say nothing about how intelligence is operationalised, how alert triage decisions are made, how human cognitive load is managed at scale, or how the security function responds when the volume and velocity of threats exceeds what human analysts can process without assistance.

For a CISO benchmarking their organisation in 2026, the relevant questions are not just “do we have a SIEM?” or “are our playbooks documented?” They are: how effectively are we using AI to reduce analyst toil? How well does our automation distinguish between genuine threats and noise? And critically, where does human judgement remain essential, and are we protecting the conditions that allow that judgement to function well?

A Revised Maturity Model: Five Levels Redefined

The framework below defines five maturity levels across four dimensions: detection capability, response capability, AI integration, and human factors. The final dimension is one that conventional models consistently overlook, and it is increasingly the differentiator between organisations that realise the value of their AI investment and those that do not.

Level 1: Reactive

At the base level, log collection is inconsistent and alerts are reviewed manually on demand rather than continuously monitored. Incident response is entirely ad hoc, with no documented playbooks and no clear escalation paths. AI and automation play no meaningful role; tools may be present but are not operationalised. Analysts are overwhelmed, fatigue is endemic, and turnover is high. The function is consuming resource without generating reliable security outcomes.

Level 2: Defined

A SIEM is in place and standard use cases have been deployed, but the false positive rate remains high and detection logic has not been meaningfully tuned to the organisation’s specific environment. Response playbooks exist on paper, but execution is largely manual and escalation paths remain unclear. Automation is limited to basic alerting rules within the SIEM. Workload is beginning to be managed, and some skills development is planned, but the function is still largely reactive in practice.

Level 3: Integrated

Telemetry coverage is broad and threat intelligence feeds have been operationalised. Detection logic is tuned and generating fewer false positives. Documented runbooks are supported by partial automation, and mean time to respond is improving measurably. AI-assisted triage is in place, SOAR has been deployed, and analyst time is increasingly focused on higher-value investigative work rather than manual alert processing. Workload is managed more deliberately, some specialisation is emerging within the team, and trust in automated tooling is beginning to develop.

Level 4: AI-Augmented

Behavioural and anomaly-based detection models are tuned to the organisation’s environment and generating a low false positive rate. Automated containment handles known threat patterns, with human oversight reserved for complex or ambiguous cases. AI is integrated across detection, triage, and response, with feedback loops in place to continuously improve model performance. Analysts are focused on work that genuinely requires human judgement. Cognitive load is actively managed, and the team understands and can interrogate the AI tooling they work alongside.

Level 5: Adaptive

Detection capability improves continuously, with adversarial simulation informing model tuning and full visibility across the attack surface. Response is near-autonomous within defined parameters, with human decision-making reserved for novel or high-impact scenarios that require contextual judgement. AI is treated as a strategic capability, with explainability and bias monitoring in place and a mature governance model governing its use. The function attracts and retains strong talent, maintains a clear model for human-AI collaboration, and develops skills continuously in response to the evolving threat landscape.

The Human Factors Dimension: What the Models Miss

Every SecOps function, regardless of how sophisticated its tooling, ultimately depends on human beings making good decisions under pressure. The AI augmentation conversation has, in many organisations, focused almost exclusively on reducing the burden on analysts. That is necessary but insufficient.

The organisations that score highest on genuine maturity in 2026 are those that have thought carefully about the conditions under which their analysts perform well, not just the tools those analysts are given. That means addressing alert fatigue not just through better filtering, but through workload design. It means ensuring that the move toward automation does not erode the deep investigative skills that are essential when novel threats appear. And it means building psychological safety into the SOC culture so that analysts feel able to escalate uncertainty rather than resolving it by closing tickets.

This is not a soft concern. Organisations that neglect the human factors dimension will find that their AI investment underperforms, because the feedback loops that make AI models improve over time depend on skilled humans correcting, refining, and contextualising automated outputs.

Where Most Organisations Actually Are

Based on conversations across sectors, the honest picture is that the majority of UK enterprises currently sit between Level 2 and Level 3 on this framework. They have a SIEM. They have some playbooks. They are beginning to deploy SOAR or AI-assisted triage tooling. But their detection logic is still generating significant noise, their automation coverage is patchy, and their AI deployments are often pilot projects that have not yet been fully operationalised.

The gap between Level 3 and Level 4 is where genuine strategic investment is required. It is not primarily a technology gap. Organisations at Level 3 typically have adequate tooling. The gap is in data quality, process maturity, and the organisational confidence to trust automated decision-making in defined scenarios. Building that confidence requires transparency in how AI models reach conclusions, governance frameworks that define the boundaries of autonomous action, and a track record of measured, low-stakes automation that earns trust incrementally.

Practical Steps for CISOs Benchmarking Their Function

For a CISO seeking to use this framework practically, the starting point is an honest assessment across all four dimensions: detection, response, AI integration, and human factors. The temptation is to score the function at its highest-performing dimension. The more useful approach is to identify the dimension where the lowest score sits, because that is almost always the constraint that limits overall maturity.

A few questions worth working through with your team:

• If a novel threat actor technique appeared in your environment today, how long before it would generate an alert? How long before an analyst would act on it?
• What percentage of your alerts are closed by automation without analyst review? Of those, what proportion are genuinely benign versus suppressed because the signal was too noisy to manage?
• Can your analysts explain how your AI-assisted triage tooling reaches its conclusions? Do they trust it? Do they have a mechanism for flagging when it appears to be wrong?
• When did someone last deliberately test your detection capability using realistic adversary simulation? What did you learn?

The answers to these questions will tell you more about your actual maturity position than any tool inventory.

The Strategic Ambition: Good Is Not Static

The most important characteristic of a Level 5 SecOps function is not any specific technology or process. It is the ability to adapt continuously. The threat landscape in 2026 is not what it was in 2023, and it will not be what it is now by 2028. AI-driven attacks are becoming more sophisticated. The regulatory environment is becoming more demanding. The attack surface, spanning cloud, OT, supply chain, and identity, continues to expand.

A mature SecOps function is one that learns. It updates its detection logic when adversary techniques evolve. It revises its response playbooks when post-incident reviews identify gaps. It invests in analyst capability as the skills required for effective human-AI collaboration shift over time. And it governs its AI tools with the same rigour it applies to any other critical operational system.

That is what good looks like. Not a fixed destination, but a demonstrated capacity to keep up, and occasionally get ahead.

Ready To Find Out More?

If you would like to work through where your organisation sits on this framework, or to explore what a realistic roadmap toward Level 4 capability looks like for your specific environment and constraints, get in touch with the HOOP Cyber team via . We work with organisations across sectors to build detection and response capabilities that are operationally effective and built for the regulatory environment ahead.