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Sign Up to ReadHybrid 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 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. 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. 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
Hybrid, 1 to 2 days a week in the office
PA Consulting
Office in United Kingdom
Also hiring in London, United Kingdom, Belfast, United Kingdom, Manchester, United Kingdom and 6 more places
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