SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals

15 questions across three pillars. Takes about 10 minutes. You'll receive a weighted maturity stage with a full scoring breakdown and priority guidance.
How well do you control, verify, and harden the AI systems and agents running in your environment?
To what extent is your security team actively deploying AI to detect, investigate, and respond to threats?
Do your policies, governance structures, and training programs keep pace with your AI usage?
The right maturity target isn't universal — it depends on your AI adoption pattern, industry, regulatory environment, and risk tolerance.
The AI Security Maturity Model uses two cap rules to prevent a strong pillar from masking a critical weakness. An organization with excellent AI-powered threat detection but no governance structure is not genuinely mature — it is structurally exposed. These rules surface those imbalances so they cannot be hidden behind a high overall average.
Without governance as the foundation, advancement is a structural risk. Severe imbalances in any pillar represent vulnerability regardless of strength elsewhere.
Your overall stage cannot exceed your Govern pillar stage by more than one. If your Govern pillar scores at Stage 2, your overall maturity caps at Stage 3 regardless of how well you score on Protect and Utilize. Governance is the policy, risk, and oversight foundation that enables and constrains both other pillars. Without it, Utilize becomes uncontrolled experimentation and Protect lacks policy authority.
Your overall stage cannot exceed your lowest individual pillar score by more than one. A program that is highly optimized in two pillars but severely underdeveloped in a third has a structural vulnerability. This rule ensures that imbalance is reflected in the final stage rather than averaged away.
Go deeper with the full framework — or talk with a SANS expert about your results.