SOC 2 Compliance for AI Systems
Trust Services Criteria — Security, Availability, Processing Integrity, Confidentiality, Privacy — apply to AI when auditors ask how models run, how outputs are authorized, and how you detect failure. TuringPulse supplies telemetry and access evidence for that layer.
Processing Integrity
Full audit trails for every AI decision: prompts, completions, tools, retrieval, policy outcomes, and human review — completeness and authorization of processing.
Security Controls
RBAC, strict tenant isolation, and policy enforcement so only authorized roles trigger high-risk actions or view sensitive traces.
Continuous Monitoring
Real-time KPIs, drift detection, and anomaly alerts for AI availability and performance — not just infrastructure uptime.
Audit Trails Auditors Can Sample
SOC 2 auditors test whether systems process data completely, accurately, and on time. For AI, that means proving what the model saw, what it did, and who approved exceptions. Immutable audit history ties each outcome to spans and policies.
- End-to-end trace linkage from user request to model and tools
- Tamper-evident timestamps and retention aligned to your policy
- Violation and override events captured for exception testing
- Search and filter attributes for population sampling
RBAC, Isolation, and Policy Enforcement
Common Criteria for access and change management expect least privilege and segregation. TuringPulse enforces tenant-scoped data paths and role-gated governance APIs so AI observability does not become a backdoor into customer data.
- Tenant-isolated queries and caches for multi-tenant SaaS
- RBAC aligned to admin, operator, and viewer personas
- Policy engine blocks or flags disallowed content and data flows
- Integration with corporate IdP patterns via standard auth flows
Monitoring and Alerts for AI SLOs
Availability TSC includes system monitoring and incident handling. Beyond pods-up metrics, TuringPulse watches model latency, error rates, evaluation regressions, and safety KPIs — the signals that indicate user-impacting failure for AI products.
- Threshold and anomaly alerts across operational and quality metrics
- Escalation paths into Slack, Teams, email, and webhooks
- Historical KPI series for control operating effectiveness
- Trace-deep links in alerts to shorten incident MTTR
