Extend existing compliance and security frameworks (e.g., PCI DSS, HIPAA, NIST AI RMF) to cover AI-specific risks.

Outcomes:

Regulatory Alignment Report: AI/ML compliance checks mapped to existing audit workflows.

Policy Recommendations: AI security incorporated into third-party risk management (TPRM) programs (e.g., BitSight, SecurityScorecard).

ML Bill of Materials (MLBoM): AI-specific asset inventory tied to existing SBOM processes for transparency.

Design secure AI/ML architectures that align with existing cloud, application security, and identity management strategies.

Outcomes:

L1 C4 Diagrams: AI security components designed to integrate with SIEM, SOAR, and existing IAM solutions(e.g., Splunk, Azure Sentinel, Okta).

Security Controls Plan: AI security mapped to zero-trust models and cloud security postures (AWS Security Hub, Microsoft Defender for Cloud).

Integration Strategy: AI model integration into existing app security (SAST, DAST, RASP) and DevOps pipelines.

Assess where AI/ML fits within business operations while integrating security considerations from existing governance, risk, and compliance (GRC) frameworks so expenditure estimates comport with reality.

Outcomes:

AI/ML Opportunity & Risk Report: Recommendations aligned with existing enterprise security policies (e.g., SOC 2, ISO 27001).

Compliance & Risk Integration: AI risk mapping into GRC workflows (e.g., Archer, ServiceNow GRC).

Cost vs. Value Analysis: Incorporates third-party risk management and vendor security frameworks.

Equip teams with AI security knowledge in a way that complements existing security training programs.

Outcomes:

Security Awareness Training: AI security modules added to enterprise security training platforms (e.g., KnowBe4, Cybrary).

Playbooks for Security Teams: AI incident response integrated with SOC workflows and existing IR processes.

AI/ML Secure Development Best Practices: AI security incorporated into existing SSDLC frameworks.

Embed AI/ML security within existing CI/CD and DevSecOps processes.

Outcomes:

Pipeline Security Roadmap: AI security seamlessly incorporated into Jenkins, GitHub Actions, GitLab CI/CD, and Azure DevOps pipelines.

Scanning & Monitoring Setup: AI model security integrated into existing security scanning tools (e.g., Snyk, Checkmarx).

Threat Detection & Response Plan: AI-specific detection rules for existing SIEM/XDR platforms.

Perform model-specific security testing within the broader application security and cybersecurity landscape.

Outcomes:

Model Security Scan Reports: Integrated with existing vulnerability management tools (e.g., Qualys, Tenable, Rapid7).

Policy-Based Blocking Implementation: AI model security enforcement within SIEM, SOAR, and endpoint security platforms.

Threat Intelligence & Attack Simulation: AI-specific attack scenarios included in existing red team/blue team exercises.