Google Secure AI Framework (SAIF): A GRC Guide

Extend AI detection and response by monitoring inputs and outputs as telemetry, using five signals—input anomalies, output anomalies, volume, threat intel, and behavior drift—to detect and respond within SOC.

Automate defenses to match adversaries' machine-speed attacks, using AI to triage, enrich, and contain incidents at scale while keeping humans in the loop.

Harmonize platform-level controls to make security a property of the organization, not individual teams. Secure-by-default platforms enforce input validation, logging, and access controls across all AI projects through the SDLC.

Contextualize AI risk by tracing data lineage and validating model behavior against business purpose in end-to-end processes, then translate technical exposure into money, regulatory, or reputational impact.

Explore the anatomy of the SAIF map, tracing data through four components—data, infrastructure, model, and application—as a flow, with risks and controls at every boundary.

Frame the model as a trained, probabilistic engine with input and output handling doors, where behavior emerges from training and is shaped by prompts and filters.

Data poisoning and unauthorized training data threaten model behavior and GDPR violations, so gate every source before ingestion with provenance, filtering, and data governance.

Discover how model source tampering and model deployment tampering create supply-chain risk, and apply integrity controls—signing, attestation, hardened serving, and monitoring—through build and deployment stages.

Explore how prompt injection works by tracing data flow from retrieval to model, distinguishing direct and indirect attacks and their impact on trusted content and risk.

Guard against insecure model output by treating every model result as untrusted, sanitizing and encoding it, and parameterizing queries to prevent injection and data loss.

Explore rogue actions as the shift from risky text to operations like moving money or changing settings, and apply least privilege, human approval, and audit logs to limit harm.

Explore denial of ml service, a dual threat to availability and cost from brute-force requests and expensive prompts; apply rate limits, caps, budgets, and anomaly detection to protect ai workloads.

Explore data provenance, licensing, and minimization to prevent data poisoning and comply with privacy rules, then ensure model integrity, access control, and output robustness through signing, attestation, and secure pipelines.

Discover how the assurance and governance wrapper coordinates data, model, infrastructure, and application controls. Red teaming and continuous evaluation keep defenses current, while logging and incident response ensure auditable accountability.

Construct a six-column control-to-risk matrix linking threats to components, control families, owners, evidence, and residual risk; demonstrates repeatable, auditable risk mitigation for Meridian's board.

Agents transition from answering to planning and acting, expanding autonomy, tool use, and memory, which widens blast radius and real-world risk across production systems.

Convert a self-assessment into a living remediation roadmap by scoring risks, clustering themes like application hardening and data governance, and sequencing controls with owners and deadlines.

Explain Meridian's saif raci and ai security operating model, with clear ownership for each activity, independent assurance, and ai risk embedded in existing grc forums as the model scales.

See how CoSAI turns SAIF into open standards under OASIS. Align SAIF with NISTAIRMF, ISO forty-two thousand one and twenty-seven thousand one, and MITRE ATLAS to stay current.

Map SAIF to the broader governance landscape by aligning its risk map and controls with NIST AI RMF, ISO 42001, ISO 27001, OWASP LLM Top Ten, and MITRE ATLAS.