# Applied AI Security Certificate Program Canonical URL: ## Overview This certificate treats AI systems as systems, covering the data, the model, the pipeline and the application rather than treating AI as a policy topic. It opens by separating traditional cybersecurity risk from genuinely AI-specific risk, then runs the AI lifecycle from design through retirement with security gates at each stage. The core is adversarial machine learning — poisoning attacks against training data, evasion attacks against deployed models, privacy attacks that target training information, and model extraction — grounded in the NIST adversarial machine learning taxonomy. Generative AI gets its own treatment: direct and indirect prompt injection, insecure output handling, and the risks introduced by retrieval-augmented generation, plugins and external data sources. It then moves from building to watching, and finally to breaking. An AI system can be compromised without anyone touching the server it runs on, so monitoring has to move from whether the system is running to whether it is behaving the way it is supposed to, on data it is supposed to see. The path covers the four categories of AI monitoring and the signals that distinguish them — a sustained spike in near-identical unusual inputs reads as adversarial probing, a slow accuracy decline reads as drift, an unexplained query spike from one credential reads as an extraction attempt — along with the evidence responders routinely fail to preserve, meaning model version, prompt and response logs, training data lineage and guardrail configuration. Severity is classified by impact rather than technical complexity, on the principle that an incorrect benefits determination caused by a data integrity failure is high severity even though nothing technically broke. It closes on adversarial testing and assurance, separating two things agencies routinely conflate: red teaming is deliberately trying to break a system, while assurance is the documented evidence that it is trustworthy enough for its purpose. That means the engagement lifecycle, rules of engagement, and the assurance case as claim, argument and evidence in a form an oversight body can audit — then extends everything to foundation models and to agentic AI, where the attack surface expands from what a system says to what a system does. ## What you'll learn - Identify the components of an AI/ML system and distinguish models, datasets, pipelines, applications and infrastructure; Distinguish traditional cybersecurity risk from genuinely AI-specific risk; Map security considerations across the AI lifecycle from design through retirement - Explain poisoning, evasion, privacy and model-extraction attacks, and apply defenses that increase robustness; Treat models, weights and checkpoints as protected assets, and verify provenance before deployment; Evaluate third-party models, datasets, libraries and frameworks as supply-chain dependencies - Identify direct and indirect prompt injection, insecure output handling, and retrieval-augmented generation risk; Distinguish the four categories of AI monitoring and select the technique that fits each; Read monitoring signals and tell adversarial probing from model drift from an extraction attempt - Preserve the evidence unique to AI incidents, including model version, prompt and response logs, and training data lineage; Classify incident severity by impact on safety, rights, mission and data rather than technical complexity - Select containment actions specific to AI, including model rollback and disabling automated decisioning; Run a red-team engagement lifecycle from planning through reporting - Write rules of engagement, and handle team composition and deconfliction; Weigh manual expert-driven testing against automated and tool-assisted approaches - Tell a genuine red-team finding apart from an ordinary software bug; Build Test, Evaluation, Verification and Validation records that support a risk decision - Construct an assurance case as claim, argument and evidence an oversight body can audit; Extend adversarial testing to foundation models and to agentic AI ## Curriculum 1. **Securing AI/ML Systems: Protecting the Intelligent Attack Surface Course** 2. **AI Red Teaming and Assurance Course** 3. **AI Security Monitoring and Incident Response Course** ## Pricing **Tuition:** $2299