Securing AI from data to deployment.
A pragmatic, framework-aligned approach to AI risk — protecting models, the data they learn from, and the systems they influence.
The Secure AI Lifecycle
From discovery to decommission — embedded security and governance controls aligned to the NIST AI Risk Management Framework, ISO/IEC 42001, and emerging EU AI Act obligations.
Policies, principles, and accountability — board-level oversight aligned to NIST AI RMF & ISO 42001.
AI inventory, context, and risk classification across business units, models, and data sources.
Assurance testing, red teaming, bias evaluation, and continuous model performance monitoring.
Lifecycle controls, secure MLOps, incident response, and supply-chain risk for foundation models.
AI Threat Modeling
STRIDE-style modeling extended for ML pipelines, prompts, and retrieval systems.
AI Red Teaming
Adversarial testing of foundation models, agents, and copilots prior to release.
Model & Data Lineage
Provenance, evaluation, and approval gates baked into the MLOps pipeline.