AI Security and Compliance Engineering Certification for Tech Professionals

Analyze AI-specific attack surfaces, including prompt injection, data poisoning, and model extraction.

Design comprehensive threat models using the STRIDE and MITRE ATLAS security frameworks.

Create automated security test suites to evaluate model robustness against real-world attacks.

Detect AI-specific code vulnerabilities like insecure deserialization and hardcoded secrets using Bandit, Semgrep, & implement custom rules.

Scan dependencies, generate SBOMs, harden infrastructure, and run container vulnerability scans using pip-audit, Snyk, and Trivy.

Build a production-ready CI/CD config with automated security gates and code signing that blocks vulnerable code before deployment.

Generate human-readable explanations using SHAP and LIME for stakeholder and auditor transparency.

Build robust rule-based and semantic LLM guardrails using Guardrails AI and NVIDIA NeMo.

Integrate Microsoft Presidio into RAG pipelines to automatically detect and mask sensitive PII.

Execute automated adversarial scans with Giskard to uncover hallucination, bias, jailbreaks, and safety failures in your LLMs.

Stress-test LLM defenses with real prompt injection and jailbreak techniques used against production chat models.

Interpret vulnerability reports, prioritize remediation by severity and exploitability, and document audit-ready findings.

Classify AI systems under the EU AI Act to determine exact legal obligations and risk tiers.

Map your AI engineering lifecycle to the NIST AI RMF (Govern, Map, Measure, Manage) functions.

Produce compliance documentation, risk assessment artifacts, and a training-data licensing review.

Execute automated adversarial red teaming scans using Giskard to identify vulnerabilities.

Draft comprehensive transparency reports, system documentation, and model cards.

Compile a complete, structured evidence package ready for EU AI Act conformity assessments.