VAPT + AI Security ( Pentesting with AI Experience)

Role Purpose

The Assistant Manager / Manager AI Security is responsible for leading the design, assessment, and validation of secure AI systems across their lifecycle. The role combines AI security architecture, secure-by-design advisory, AI security tooling, and AI red teaming to help organisations deploy AI safely, securely, and at scale.

Key Responsibilities

1 AI Security Architecture & Secure-by-Design Advisory

Lead and review AI security architecture for:

• AI platforms, pipelines, and MLOps environments
• GenAI applications (LLMs, copilots, agents, RAG pipelines)

Define and embed securebydesign principles across:

• Data ingestion and training pipelines
• Model hosting, inference APIs, and AI agents
• Identity, access control, and isolation for AI systems

Advise clients on defenseindepth approaches aligned to:

• Zero Trust (NHI, IAM) for AI
• Enterprise cloud and application security patterns
• AIspecific threat models

2 AI Security Across the AI Lifecycle

Data & Training Phase

• Assess risks related to:
• Data poisoning, bias manipulation, and training data leakage
• Weak provenance, lineage, and thirdparty data dependencies
• Define controls for secure data handling and integrity validation.

Model Development & FineTuning

• Perform AI threat modeling covering:
• Model inversion and extraction
• Backdoored or trojaned models
• Review secure MLOps pipelines, CI/CD controls, and isolation mechanisms.

Deployment & Inference

• Secure AI runtime environments across cloud, containerized, and hybrid setups.
• Define controls for:
• Prompt injection resistance
• Output filtering, guardrails, and misuse prevention
• Abuse scenarios in GenAI and agentbased systems

Monitoring & Operations

• Support AI Security Posture Management (AISPM) and continuous assurance.

AI Red Teaming & Adversarial Testing

Plan and execute AI Red Team engagements, including:

• Prompt injection and jailbreak testing
• Model misuse, data exfiltration, and policy bypass scenarios
• AIenabled attack simulations

Apply industry frameworks such as:

• MITRE ATLAS
• OWASP Top 10 for LLM Applications
• OWASP Agentic AI
• NIST AI RMF

Design custom adversarial scenarios aligned to client context and risk appetite.

Translate red team findings into actionable remediation roadmaps for engineering and leadership teams.

Engagement Leadership & Practice Contribution

• Manage small to midsized AI security engagements or workstreams.
• Act as a subjectmatter resource for AI security across pursuits and proposals.
• Mentor junior team members on AI security concepts and delivery.
• Contribute to thought leadership, playbooks, and internal enablement on AI security and red teaming.

Required Skills & Experience

Strong understanding of cybersecurity fundamentals (IAM, AppSec, Cloud Security, Threat Modeling).

Practical understanding of AI/ML and GenAI architectures, including:

• Model training vs inference
• LLMs, agents, and RAG patterns

Experience with:

• AI security assessments or testing
• Secure MLOps / DevSecOps pipelines
• Offensive security or adversarial testing (preferred)

Preferred Certifications

• CAI/ML Pen Certificate (Certified AI/ML Penetration Testing)
  • CEH, OSCP, or equivalent offensive security certification
  • GIAC certifications (GPEN, GWAPT, GCTI)
  • Cloud Security certifications (AWS / Azure / GCP)
  • AI Governance / Responsible AI certifications or NIST AI RMF training
  • CISSP / CCSP (advantageous)