SecureAIScore Evaluation Methodology
Original methodology for evaluating enterprise AI security and governance capabilities.
Methodology Overview
SecureAIScore methodology combines qualitative and operational control evaluation across thirteen enterprise pillars.
Evaluation Pillars
Security
Why it matters: Security controls define baseline resilience for AI systems.
How to evaluate: Review control coverage across model access, runtime events, and incident workflows.
Common mistakes: Assuming generic security controls fully cover AI-specific threat surfaces.
Governance
Why it matters: Governance ensures accountable AI decision-making.
How to evaluate: Assess ownership clarity, policy lifecycle, and exception management discipline.
Common mistakes: Publishing policy documents without operational enforcement.
Compliance
Why it matters: Compliance readiness reduces regulatory and audit friction.
How to evaluate: Map controls to required frameworks and evidence collection workflows.
Common mistakes: Treating compliance as a one-time documentation exercise.
Scalability
Why it matters: Scalable controls support growth without risk drift.
How to evaluate: Validate architecture patterns under increased usage and team expansion.
Common mistakes: Relying on manual controls that cannot scale with adoption.
Deployment
Why it matters: Deployment flexibility influences security and data boundary choices.
How to evaluate: Compare cloud, hybrid, and on-prem options against risk and legal requirements.
Common mistakes: Selecting deployment models before defining risk constraints.
Identity
Why it matters: Identity controls are core to AI access governance.
How to evaluate: Measure RBAC quality, privileged access controls, and recertification processes.
Common mistakes: Using broad access roles for operational convenience.
Monitoring
Why it matters: Monitoring enables detection and continuous improvement.
How to evaluate: Assess telemetry depth, alert quality, and investigation workflows.
Common mistakes: Collecting logs without defined response actions.
Data Protection
Why it matters: Data protection controls reduce leakage and misuse risk.
How to evaluate: Test prompt, context, and output controls against data sensitivity policies.
Common mistakes: Applying static DLP rules to dynamic AI workflows without tuning.
Operational Readiness
Why it matters: Operational readiness ensures repeatable execution.
How to evaluate: Evaluate playbooks, training, and cross-team response alignment.
Common mistakes: Ignoring handoff friction between security and engineering.
Integration
Why it matters: Integrations determine practical control effectiveness.
How to evaluate: Review interoperability with IAM, SIEM, GRC, and ticketing systems.
Common mistakes: Treating integration as post-deployment enhancement.
Enterprise Support
Why it matters: Support quality affects adoption and resilience.
How to evaluate: Assess support SLAs, escalation channels, and enablement depth.
Common mistakes: Underestimating operational dependency on vendor support.
Transparency
Why it matters: Transparency is essential for trust and auditability.
How to evaluate: Inspect policy explainability, logging clarity, and decision traceability.
Common mistakes: Using opaque controls with limited audit context.
Risk Management
Why it matters: Risk alignment connects AI programs to executive priorities.
How to evaluate: Measure risk identification, prioritization, and treatment governance.
Common mistakes: Tracking AI risks separately from enterprise risk governance.
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