Function: Digital Trust / Cyber Defense / Emerging Technology Security

Experience: Assistant Manager (3 to 5 years) | Manager (5 to 8 years)

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 secure by design 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 defense in depth 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:

o Data poisoning, bias manipulation, and training data leakage

o Weak provenance, lineage, and thirdparty data dependencies

Define controls for secure data handling and integrity validation.

Model Development & FineTuning

Perform AI threat modeling covering:

o Model inversion and extraction

o 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:

o Prompt injection resistance

o Output filtering, guardrails, and misuse prevention

o 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)

Personal Attributes

- Robust analytical and problem-solving mindset for emerging and ambiguous risks.
- Ability to communicate deep technical issues to senior stakeholders.
- Comfortable operating at the intersection of security, data science, and engineering.

📌 VAPT + AI Security Professional (Mumbai)
🏢 Contactx Resource Management
📍 Mumbai