MAESTRO
Threat model AI systems across the full operating stack.
Gamut uses MAESTRO-style threat modelling to help security and assurance teams assess how AI systems can fail, be misused or be attacked across model, data, agent, infrastructure, observability and ecosystem layers.
Discuss AI threat modellingThe seven-layer view
Foundation models
Assess risks around model behavior, misuse, robustness, unsafe outputs and model-provider dependencies.
Data operations
Review training, retrieval, context, lineage, quality, leakage and data governance threats.
Agent frameworks
Assess planning, delegation, memory, tool use, autonomy, prompt flow and agent orchestration risk.
Deployment infrastructure
Threat model APIs, runtime, cloud, endpoints, identity, secrets, logging and environment boundaries.
Security and compliance
Connect threats to obligations, controls, evidence, findings, approvals and assurance review.
Evaluation and observability
Assess monitoring, testing, telemetry, drift, incident detection, investigation and feedback loops.
How Gamut operationalizes MAESTRO
Threat records
Capture likelihood, impact, notes, affected systems, owners and supporting evidence for each relevant threat.
Control linkage
Connect threat findings to GTSAF controls, ATF runtime boundaries, Gateway approvals and remediation work.
Security reporting
Produce summaries for CISOs, security architecture, risk, audit and buyer-assurance teams.
Continuous review
Refresh threat posture as agents, models, tools, connectors and data sources change.
Acknowledgement
Gamut's MAESTRO-oriented workflow acknowledges MAESTRO as a Cloud Security Alliance AI threat modelling framework created by Ken Huang / DistributedApps.ai. Gamut uses MAESTRO concepts for product workflow, assessment and assurance support; this page is not an endorsement by the framework authors or Cloud Security Alliance.