Senior Associate, National Security-Cyber Security Governance

About the role

Alvarez & Marsal (A&M) is a global consulting firm with over 10,000 entrepreneurial, action and results-oriented professionals in over 40 countries. We take a hands-on approach to solving our clients' problems and assisting them in reaching their potential. Our culture celebrates independent thinkers and doers who positively impact our clients and shape our industry.

Responsibilities

• Lead technical teams in executing AI security assessments, model audits, and compliance reviews related to AI Act (EU), NIST AI Risk Management Framework, ISO/IEC 23053/23894, and emerging AI governance standards.
• Develop AI risk assessment methodologies and implement continuous monitoring solutions for production ML systems.
• Design and implement secure AI/ML architectures incorporating MLOps security practices, including model versioning, data lineage tracking, feature store security, and secure model deployment pipelines.
• Integrate security controls for Large Language Models (LLMs), including prompt injection prevention, output filtering, and embedding security.
• Conduct technical assessments of AI/ML systems using tools such as: AI Security Tools: Adversarial Robustness Toolbox (ART), Foolbox, CleverHans for adversarial testing MLOps Platforms: MLflow, Kubeflow, Amazon SageMaker, Azure ML, Google Vertex AI Model Monitoring: Evidently AI, Fiddler AI, WhyLabs, Neptune.ai for drift detection and explainability LLM Security: Guardrails AI, NeMo Guardrails, LangChain security modules, OWASP LLM Top 10 tools Privacy-Preserving ML: PySyft, TensorFlow Privacy, Opacus for differential privacy implementation
• Implement AI compliance and governance solutions addressing: Regulatory Frameworks: EU AI Act, Canada's AIDA, US AI Executive Orders, Singapore's Model AI Governance Framework Industry Standards: ISO/IEC 23053, ISO/IEC 23894, IEEE 7000 series, NIST AI RMF Sector-Specific Requirements: FDA AI/ML medical device regulations, GDPR Article 22 (automated decision-making), SR 11-7 model risk management
• Conduct penetration testing specifically for AI systems, including: Model extraction attacks and defenses Data poisoning vulnerability assessments Membership inference and model inversion testing Prompt injection and jailbreaking assessments for LLMs Backdoor detection in neural networks Program and deploy custom security solutions using: Languages: Python (PyTorch, TensorFlow, scikit-learn), R, Julia AI Frameworks: Hugging Face Transformers, LangChain, LlamaIndex, AutoML tools Security Libraries: SHAP, LIME for explainability; Fairlearn, AIF360 for bias detection Infrastructure: Docker, Kubernetes, Terraform for secure AI deployment
• Integrate AI security with traditional security frameworks including Zero Trust architecture, IAM solutions, and SIEM platforms.
• Implement automated compliance monitoring using AI-powered security orchestration tools (SOAR platforms like Splunk Phantom, Palo Alto Cortex XSOAR).
• Assess and mitigate risks in: Foundation models and transfer learning implementations Federated learning systems Edge AI deployments Multi-modal AI systems Generative AI applications (GPT, DALL-E, Stable Diffusion implementations)
• Create technical documentation including AI system security architecture reviews, threat models specific to ML pipelines, compliance mappings, and remediation roadmaps aligned with both traditional security standards (NIST 800-53, ISO 27001) and AI-specific frameworks.

Qualifications

• 3+ years of experience in AI/ML development, deployment, or security assessment
• 2+ years of experience in information security, with focus on application security or cloud security
• Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face)
• Proficiency in Python programming with experience in AI/ML libraries and security testing tools
• Experience with cloud AI platforms (AWS SageMaker, Azure ML, Google Vertex AI, Databricks)
• Knowledge of AI compliance frameworks: NIST AI RMF, EU AI Act requirements, ISO/IEC 23053/23894
• Experience with MLOps tools and secure model deployment practices
• Understanding of adversarial machine learning and AI security threats (OWASP ML Top 10, ATLAS framework)
• Familiarity with privacy-preserving ML techniques (differential privacy, federated learning, homomorphic encryption basics)
• Experience with containerization (Docker, Kubernetes) and infrastructure as code
• Knowledge of traditional security frameworks (NIST CSF, NIST 800-53, ISO 27001)
• Ability to obtain a USG security clearance

Preferred Certifications

• One or more AI/ML certifications: AWS Certified Machine Learning, Google Cloud Professional ML Engineer, Azure AI Engineer
• Security certifications: CISSP, CCSP, CompTIA Security+
• Specialized: GIAC AI Security Essentials (GAISE), Certified AI Auditor (when available)