AI Security Engineer

Global Innovation and Technology Centre, Back Lane, Melbourn, Hertfordshire, SG8 6DP
, United Kingdom, Melbourn SG8 6DP
, GB

We believe in the power of ingenuity to build a positive human future.

As strategies, technologies, and innovation collide, we create opportunity from complexity.

Our teams of interdisciplinary experts combine innovative thinking and breakthrough technologies to progress further, faster. Our clients adapt and transform, and together we achieve enduring results.

We are over 4,000 strategists, innovators, designers, consultants, digital experts, scientists, engineers, and technologists. And we have deep expertise in consumer and manufacturing, defence and security, energy and utilities, financial services, government and public services, health and life sciences, and transport.

Our teams operate globally from offices across the UK, Ireland, US, Nordics, and Netherlands.

PA. Bringing Ingenuity to Life.

Hybrid working - our approach is to be in the office a minimum of 2 days per week.

PA’s IT team, internally known as Group Systems, is comprised of a variety of areas that support the wider firm, consisting of four key pillars:
Strategy, Architecture and Infrastructure Services;
Enterprise Solutions;
Technology and Operations; and Cyber Security and Compliance.

The successful candidate will be covered two primary areas: the security of AI (protecting the organisation's own AI systems and use of AI) and AI for security (using AI to strengthen protection, detection and response). This role will sit within Cyber Security and Compliance.

Technical ownership of AI security tool selection and deployment

Define requirements, run structured evaluations and proofs of concept, and judge vendor claims on evidence rather than marketing.

That means measuring detection efficacy, false positive rates, latency and operational overhead. The tool categories to cover include AI security posture management, LLM guardrails and firewalls, model and supply-chain scanning, GenAI data loss prevention, and AI-enabled SOC tooling.

They should also own the deployment itself: integration with existing SIEM, SOAR, EDR and identity tooling, architecture decisions, and making sure the tools are tuned and producing value after go-live rather than becoming shelfware.

Input to vendor risk assessments fits here too, particularly on how vendors handle your data in their own models.

Understanding the AI threat landscape

They need a working knowledge of AI-specific attack techniques and should be fluent in the reference frameworks, mainly the OWASP Top 10 for LLM Applications and MITRE ATLAS. Key threats include:

• 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. 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 protection, that might mean AI-assisted vulnerability prioritisation, secure code review, or posture analysis. In detection, it covers behavioural anomaly detection, alert triage and enrichment, and LLM-assisted threat hunting. In response, it includes AI copilots for investigation and incident summarisation, automated playbooks, and agentic response actions.

A good candidate will be clear-eyed about…