Enterprise AI Security and LLM Governance

AI systems introduce new attack vectors: (1) Prompt Injection - adversarial inputs that manipulate model behavior (like SQL injection for LLMs); (2) Training Data Extraction - attackers can infer confidential information from model responses; (3) Model Poisoning - corrupted training data degrades model quality; (4) Adversarial Inputs - specially crafted inputs cause misclassification; (5) Supply Chain Risk - reliance on 3rd-party LLM providers (ChatGPT, Claude, Copilot) creates trust boundary risks. Unlike traditional apps, LLMs can be exploited in non-obvious, difficult-to-predict ways due to their probabilistic nature.

Yes, absolutely. Bill 25 Article 4 directly applies to AI-based decision-making. Key requirements: (1) Transparency - individuals must be informed when decisions are made by algorithms; (2) Rights - Quebec residents can request explanation of how an algorithm made a decision about them; (3) Accountability - organizations must document AI decision logic and maintain audit trails; (4) Consent - for high-risk processing (e.g., credit decisions), explicit consent is required. We help you implement compliance procedures including processing registers, PIAs (Privacy Impact Assessments), and decision logging.

We employ multiple complementary techniques: (1) Prompt Injection Testing - we craft adversarial prompts to bypass guardrails and extract confidential information; (2) Jailbreak Testing - we attempt to manipulate the model into ignoring safety constraints; (3) Data Extraction Validation - we test for training data leakage and model inversion attacks; (4) Robustness Analysis - we generate adversarial inputs to test model stability; (5) Bias & Fairness Audit - we validate that model decisions don't discriminate against protected groups; (6) Supply Chain Assessment - we evaluate risk from 3rd-party LLM provider terms, data handling, and security controls. Our tests mimic realistic attacks that adversaries could perform.

Absolutely. We audit your implementations and integrations of proprietary LLMs, not the models themselves. We assess: (1) Prompt Chain Security - validate that your prompts don't leak confidential data; (2) Input Sanitization - ensure user inputs are properly cleaned before reaching the LLM; (3) Output Handling - validate that model responses are filtered and logged appropriately; (4) API Security - test authentication, rate limiting, and access controls; (5) Data Governance - ensure compliance with LLM provider terms (e.g., data retention policies); (6) Vendor Risk - evaluate LLM provider's security posture and certifications. You maintain control of your data; we validate how it flows through the LLM integration.

Timing depends on scope. An express audit can cover critical risks, shadow AI inventory and Bill 25 readiness; a standard or deep assessment adds broader testing and security policy work. We confirm the timeline and deliverables after reviewing the environment.