AI Security Architect

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 Dev Sec Ops  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 Tensor Flow, 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 Dev Sec Ops  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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