AI deployment is outpacing governance, leaving the VP of AI, CTO, and CISO stuck between speed, stability, and shifting regulations. We bridge this gap with deep-tech AI audits that act as the brakes, giving you the control needed to safely move faster.
Balancing Velocity and Vulnerability to Secure the AI-Powered Enterprise
We automatically catalog our entire AI footprint—from custom LLMs to vendor tools—giving us total visibility into exactly where corporate data is flowing.
We test our models against unique AI risks like prompt injection, data leakage, and bias, establishing clear guardrails before code hits production.
We deploy continuous monitoring to track live model behavior, creating an automated audit trail for instant board and regulatory compliance.
Every system is different; we tailor depth to your architecture, regulatory context, and threat model. These are recurring themes in audits where harm, scale, or sensitivity is elevated.
We trace provenance, retention, and access for training and production data. Labeling quality, retrieval refreshes, and environment separation are checked so leakage and poisoning cannot hide in handoffs or shared stores.
We stress-test robustness, drift detection, misuse paths, and guardrails across APIs, chat, and batch jobs. Findings map to concrete controls and replayable evidence so ML and security can verify fixes after remediation.
We align minimization and access control to prompts, telemetry, embeddings, and logs as shipped. Inference-time exposure and logging proportionality are reviewed so privacy risk is clear to engineering and DPOs.
We inventory hosted models, datasets, vendors, and CI/CD update paths against your trust boundaries. Weak provenance, privileged integrations, or fragile approvals are ranked before a single failure can cascade.
Traditional risk management wasn't built for software that learns dynamically. When neural networks evolve post-deployment, static check-boxes fail—creating a false sense of security while leaving models exposed to real-world data leaks.
Translates complex model behaviors into clear business risks, helping the C-suite align on priorities, sign off safely, and report confidently to the Board.
A prioritized, step-by-step guide to patch vulnerabilities like prompt injection, data exposure, and model hallucinations.
A customized blueprint mapped to ISO 42001 and NIST RMF, shifting your team from static check-boxes to continuous runtime tracking.
“Defensible audits name the threat model, show what was exercised, and separate unknowns from confirmed gaps. That is the bar we design for.”AI Security audit practice
High-risk systems deserve reviews that withstand scrutiny from customers, regulators, and your own engineers. Share your constraints and timelines—we’ll propose a proportional assessment path.
Contact AI SecuritySame methodology, adjusted intensity, whether you ship models, embed them in products, or operate them internally at scale.
Hands-on sessions, architecture walkthroughs, and concrete feedback woven into how you iterate. Useful when releasing new modalities, agentic workflows, or high-sensitivity retrieval stacks.
Explore training offeringsBoard-ready summaries, dependency visibility, and alignment to internal controls frameworks—without losing fidelity for the practitioners who patch issues.
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