Responsible Artificial Intelligence Maturity Matrix

Two prominent Maturity Models from the ISACA ecosystem stand out — but they serve fundamentally different purposes. The Capability Maturity Model Integration (CMMI) is a battle-tested organizational process maturity model. The ISACA Advanced in AI Security Management (AAISM) is a professional certification focused on individual expertise in AI security.

While both address governance, risk, and controls in the AI era (especially with the recent launch of CMMI’s Artificial Intelligence Maturity (AIM) extension), they differ sharply in scope, structure, and intent. It helps organizations move from ad-hoc, unpredictable operations to disciplined, measurable, and continuously improving processes.

Core elements:

• 5 Maturity Levels (staged representation): Initial (1), Managed (2), Defined (3), Quantitatively Managed (4), Optimizing (5).

• Practice areas are organized into categories (Process Management, Project Management, Engineering, Support).

Intent: Drive organizational capability and performance improvement. Higher maturity leads to better predictability, quality, reduced risk, and stronger business outcomes.

The new CMMI AIM (Artificial Intelligence Maturity) model extends this by integrating AI-specific best practices into the existing framework. It provides structured pathways for AI governance, implementation, risk management, and value realization, with capability levels and independent appraisals.

AAISM (Advanced in AI Security Management) is ISACA’s first AI-centric security management certification, focusing on managing AI-specific risks and governance.

Core elements:

• 5 Maturity Levels (staged representation): Baseline (1), Emerging (2), Developing (3), Realizing (4), Leading (5).

• Practice areas are organized into categories (Governance and Program Management, Risk, Technologies, and Controls).

Intent: Equip individual security leaders with the knowledge and credibility to identify, assess, monitor, and mitigate AI-specific threats while ensuring responsible enterprise AI adoption. It supplements existing security management expertise rather than replacing broader frameworks.

CMMI and its AIM extension	AAISM
1.      Initial: Processes are unpredictable, reactive, and often lack formal planning.  Success depends on individual effort.	1.      Baseline: Little to no understanding of responsible artificial intelligence. No formal policies or processes and initially focused on data and privacy.
2.      Managed: Processes are planned, performed, measured, and controlled, but not enterprise-wide.  Basic project management concepts followed.	2.      Emerging: Foundational policies and processes are established, and risk assessments are performed for in-scope systems.
3.      Defined: Processes are well characterized, understood, and standardized. Establishes a uniform set of organizational standards. This shifts the organization to a proactive stance.	3.      Developing: Good understanding of responsible AI. Fairness, privacy, security, accuracy, oversight, transparency, and accountability are considered.
4.      Quantitatively Managed: The organization sets and manages quantitative (measurable) objectives for both process performance and product quality. Statistical and quantitative techniques are heavily utilized.	4.      Realizing: Enterprise-wide understanding of responsible AI. Fairness, privacy, security, accuracy, oversight, transparency, and accountability are addressed. Some best practices are implemented.
5.      Optimizing: The organization is entirely focused on continuous process improvement. Processes are continually refined based on a quantitative understanding of common causes of process variation.	5.      Leading: Thorough understanding of responsible AI. Best practices implemented across the program, ongoing risk assessment, and diverse stakeholder feedback. Make responsible AI practices better.

Key Takeaways for Practitioners and Leaders

• CMMI is about the “how” of organizational discipline. It provides a roadmap for building mature processes that can sustain AI initiatives at scale. The new AIM extension makes it particularly relevant for organizations wanting to govern AI like any other critical capability.

• AAISM is about the “who” — the skilled professionals. It ensures that security managers understand AI-specific threats (e.g., model poisoning, data leakage during training, adversarial attacks, supply chain risks with AI vendors) and can translate governance principles into actionable controls and policies.

• They are complementary, not competing. A mature organization (high CMMI level) benefits enormously from having AAISM-certified leaders driving its AI security program. Conversely, AAISM-certified professionals can help accelerate an organization’s journey toward higher CMMI/AIM maturity in AI-related practice areas.