Aligning AI Adoption Maturity with AI Security Using CMMI and Russian Regulatory Requirements

Organizations today operate simultaneously in two distinct dimensions of artificial intelligence: the depth of AI integration into business processes and the maturity of security controls protecting those systems. These two maturity levels rarely align, and the resulting gap is responsible for most security incidents and regulatory findings rather than the mere fact of using AI.

To make the misalignment visible, both axes are mapped onto the five-level CMMI scale. Level L1 represents initial, unpredictable processes; L2 covers managed processes at the project level; L3 introduces organization-wide defined standards; L4 adds quantitative management through metrics; and L5 focuses on continuous optimization.

Under this unified scale, typical adoption follows the Gartner progression from awareness to enterprise transformation, while security maturity is assessed using OWASP AIMA across eight domains including Governance, Data Management, and Operations. The mapping reveals that many organizations reach L3 in adoption while remaining at L1 or L2 in security, leaving them exposed to Shadow AI and violations of mandatory requirements.

FSTEC Order No. 117, effective from March 2026, replaces earlier rules and introduces explicit obligations in paragraphs 60 and 61. These require the use of trusted AI technologies, prohibition on transferring restricted data to model developers, statistical criteria for response accuracy, and controls on query formats. The obligations activate when systems move from pilot to production use, exactly at the L2-to-L3 transition.

Additional Russian anchors include GOST R 56939-2024 for secure development, GOST R 59276-2020 for trust mechanisms, and the new P NST 1046-2026 standard for AI in critical information infrastructure. International references such as Google SAIF, NIST AI RMF, and MITRE ATLAS supply supporting risk maps and attack catalogs that fit within the same CMMI framework.

The practical states organizations pass through include denial of AI use, widespread uncontrolled Shadow AI, LLM controls that leave agentic systems exposed, and finally managed agentic environments with least-privilege tool access and continuous red-teaming. The recommended approach is to ensure security maturity matches or slightly exceeds adoption maturity at every level to avoid both operational risk and regulatory non-compliance.