The AI Security Architect will play a pivotal role in designing and implementing secure AI/ML architectures for a next-generation platform development. This position bridges artificial intelligence engineering and cybersecurity architecture, ensuring that all AI-driven models, data pipelines, and automation frameworks are resilient, explainable, and secure by design.
The architect will work closely with data scientists, platform engineers, CTI analysts, and DevSecOps teams to define end-to-end AI security standards — covering areas such as model lifecycle security, data protection, adversarial defense, and ethical AI governance. The goal is to embed trust, compliance, and robustness within every AI-powered component of the platform.
Requirements 1. AI Security Architecture Design
- Define and implement a secure AI/ML architecture framework across platform components.
- Architect end-to-end MLOps pipelines that ensure data integrity, provenance, and secure deployment.
- Design defensive mechanisms against model poisoning, prompt injection, data drift, and adversarial ML attacks.
- Establish patterns for secure inference, retraining, and version control of AI models.
2. Secure AI & Data Governance
- Collaborate with data engineers to enforce data lineage, encryption, and anonymization policies in ML pipelines.
- Define and implement AI governance and compliance frameworks (NIST AI RMF, ISO/IEC 42001).
- Establish explainability (XAI) and auditability controls for all deployed AI/ML models.
3. Integration with the CTI Platform Stack
- Embed AI capabilities into key product modules, including:
- Threat scoring and correlation engines
- Predictive and anomaly detection systems
- AI-driven narrative generation
- Enrichment and automated decisioning pipelines
- Collaborate with backend engineers to secure API, microservice, and model interfaces.
4. Risk, Compliance & Threat Modeling
- Conduct threat modeling and risk assessments for AI and data workflows using STRIDE or MITRE ATLAS.
- Develop an AI risk register with mitigation strategies and continuous monitoring.
- Partner with Red Team and Security Engineering functions to test and harden AI pipelines against abuse.
5. Cross-Functional Leadership
- Act as a bridge between AI/ML development and cybersecurity operations.
- Advise product teams on secure AI implementation standards and model risk management.
- Mentor engineers and data scientists in secure AI development practices.
Desired Skills & Expertise Technical Competencies - Strong experience designing AI/ML architectures using frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Proficiency in Python, microservices, and API security (FastAPI/Flask).
- Deep understanding of adversarial ML techniques, model inversion, data poisoning, and prompt injection attacks.
- Experience integrating and securing LLMs or NLP-based components in production systems.
- Familiarity with data pipeline and orchestration tools (Kafka, Airflow, Elasticsearch, Neo4j).
- Hands‑on exposure to containerization, orchestration, and infrastructure security (Docker, Kubernetes).
Cybersecurity Skills - Experience in application security, identity & access control, and DevSecOps processes.
- Working knowledge of MITRE ATLAS, OWASP AI Security Top 10, and NIST AI Risk Management Framework.
- Experience conducting architecture reviews, risk assessments, and secure SDLC integration for AI systems.
- Familiarity with MLOps security controls including model validation, versioning, and monitoring pipelines.
Soft Skills - Strong analytical and problem-solving mindset.
- Excellent communication — able to explain complex AI security issues to technical and executive audiences.
- Detail-oriented, self-driven, and capable of influencing cross‑functional technical decisions.
Education & Certifications - Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Artificial Intelligence, or related field.
- Preferred Certifications:
- Cloud AI Architect (AWS/GCP/Azure)
- CISSP, CCSP, or SABSA (for architecture alignment)
Experience Required - Minimum 5+ years of total experience in cybersecurity, AI/ML engineering, or architecture.
- At least 3 years of hands‑on experience designing or securing AI‑driven systems.
- Proven background integrating AI/ML modules into cybersecurity or analytics platforms.
- Prior exposure to CTI, SOAR, or security data platforms is highly desirable.
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