As organizations increasingly rely on complex digital systems and artificial intelligence (AI) technologies, the need for structured and continuous evaluation of cybersecurity and AI governance practices becomes paramount. Maturity assessments offer a systematic approach to measuring the effectiveness, consistency, and adaptability of an organization’s cybersecurity and AI risk management capabilities. By evaluating core domains—such as governance, risk, asset management, monitoring, and incident response—these assessments provide a comprehensive snapshot of both operational strengths and areas requiring improvement. In the context of cybersecurity, maturity assessments align with frameworks like NIST CSF 2.0 to enhance resilience, ensure regulatory compliance, and reduce exposure to cyber threats. Similarly, AI maturity assessments grounded in frameworks like the NIST AI RMF help organizations identify and mitigate risks related to bias, data integrity, model robustness, and ethical considerations. Together, these evaluations support informed decision-making, promote accountability, and lay the foundation for sustainable digital trust and responsible technology adoption.
At Assessed Intelligence, we view cybersecurity and AI evaluations through the lens of process and practice which represent two critical but distinct dimensions of organizational maturity. Process refers to the formal, documented policies, procedures, and governance structures that guide how tasks should be performed—such as incident response plans, access control frameworks, or AI model risk management protocols. These processes ensure consistency, accountability, and auditability across the organization.
Practice, on the other hand, reflects how these processes are actually carried out in day-to-day operations. It includes the behavior, skills, and consistency of personnel—whether teams follow secure coding practices, conduct fairness checks in AI models, or enforce access controls effectively.
An organization may have strong processes on paper, but if they’re not actively practiced, risk remains high. Conversely, good practices without formal processes result in inconsistent execution and limited scalability. Therefore, effective evaluations must assess both: process to verify structure and policy, and practice to confirm real-world implementation and cultural adoption. Together, they offer a complete picture of cybersecurity or AI governance maturity.
Maturity Levels: Practice vs Process
| Maturity Level | Practice – Do we do what we say | Process – What do we say we do (Policies & Procedures) |
|---|---|---|
| Level 1 Initial | Informal, undefined capabilities; ad hoc execution | Undefined or non-existent processes; no formal policies |
| Level 2 Repeatable | Consistent in pockets; not standardized org-wide | Documented in some areas; outdated policies (>2 years) |
| Level 3 Defined | Roles are assigned, trained, and standardized | Policies/procedures are standardized, approved, and aligned with strategy (<10% exceptions) |
| Level 4 Managed | Capabilities are measured, evaluated, and predictable | Compliance is enforced; processes monitored for effectiveness (<5% exceptions) |
| Level 5 Optimizing | Continuous improvement driven by internal/external insights | Policies evolve with change; metrics tracked; <1% exceptions |
Assessed Intelligence Maturity Assessment
Cybersecurity & AI
