AI Security Engineer

Responsibilities

• Direct and indirect prompt injection Data and model poisoning Sensitive data leakage through prompts and outputs Model theft and extraction Malicious model files in the supply chain The growing set of risks from agentic AI, such as excessive permissions and tool misuse They also need to understand how attackers use AI against the organisation (more convincing phishing, deepfake-enabled fraud, faster reconnaissance and exploit development) and how that changes your threat model.
• In protection, that might mean AI-assisted vulnerability prioritisation, secure code review, or posture analysis.
• In response, it includes AI copilots for investigation and incident summarisation, automated playbooks, and agentic response actions.
• Examples are testing RAG applications for injection and data exposure, or checking that an agent can't be manipulated into acting outside its intended permissions.
• Alignment with governance and standards This isn't primarily a governance role, but the engineer should translate frameworks into technical controls and evidence.

Key threats include

Shadow AI, meaning unsanctioned use of AI tools by staff, is a practical, near-term issue they should be able to discover and manage. Demonstrating AI in protection, detection and response This is where the role earns its keep with stakeholders.

In detection, it covers behavioural anomaly detection, alert triage and enrichment, and LLM-assisted threat hunting.

A good candidate will be clear-eyed about limits: where human-in-the-loop controls are needed, how to validate AI outputs, and how to avoid automating mistakes at speed. The ability to build or run demonstrators that show value concretely is a strong differentiator. Assurance and testing The role should include adversarial testing and red teaming of the organisation's AI systems, whether done directly or by scoping and overseeing third parties.

The relevant reference points are

NCSC's Guidelines for Secure AI System Development The UK government's AI Cyber Security Code of Practice NIST AI RMF ISO/IEC 42001 The EU AI Act, where relevant to clients or operations

Skills and background The ideal profile is a security engineer (typically from cloud security, security architecture, or SOC engineering) who has built genuine ML and LLM literacy. That means understanding how models, RAG pipelines and agents work, reasonable Python or scripting ability, and cloud platform experience.

Communication matters more than usual

this person will regularly need to explain AI risk and capability to non-specialist decision-makers without overselling or scaremongering.

Measures of success Useful indicators

Tools selected and deployed against defined requirements, with measurable outcomes (reduced triage time, improved detection coverage) An AI asset inventory and threat model in place AI systems tested before deployment Demonstrators that have influenced investment or adoption decisions We know the skill-gap and ‘somewhat need to tick every box’ can get in the way of meeting brilliant candidates, so please don’t hesitate to apply – we’d love to hear from you. Apply today by completing our online application Please be aware that some of our UK roles at PA Consulting require a UK security clearance. All PA people are required to undergo background checks and to achieve the Baseline Personnel Security Standard however, some UK roles also require higher levels of National Security Vetting, where applicants must have at least 5 years of continuous residency in the UK. We therefore ask that you only apply if you meet the residency requirements (i.e. you are a British citizen or have been resident in the UK for the past 5 years), as this is the prerequisite for a security clearance. If you're unsure about your eligibility, we encourage you to review the UK Government’s guidance on security vetting before applying.

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