Artificial Intelligence Security Engineer

An AI security engineer works on keeping machine learning systems safe from attack and misuse - both protecting the models and data an organisation builds, and defending against people who try to manipulate them. In practice this means testing AI systems for weaknesses (for example, getting a chatbot to ignore its instructions or leak private data), building guardrails and monitoring around them, and advising the engineers who ship AI products on how to do it securely. Employers include banks, cloud and software companies, AI labs, defence and government bodies, consultancies and specialist security firms.

Approximate graduate salary

Typically somewhere around GBP 30,000-45,000 to start, though this varies a great deal. Graduate schemes at large banks, consultancies and technology firms tend to sit at the upper end, often with London weighting; public sector and defence roles and smaller regional employers tend to start lower. Specialist AI and security firms sometimes pay above this range for candidates with strong demonstrable skills, and pay in this field tends to rise quickly once you have a few years of experience.

What you'd actually do

Attempting to break an AI system deliberately - feeding a large language model crafted inputs to make it bypass its safety rules ('prompt injection' and 'jailbreaking'), or testing whether a model can be made to reveal training data or confidential information - and writing up what worked
Reviewing how a model is deployed: who can call it, what data it can reach, what tools or databases it is connected to, and whether a compromised model could be used to reach something more sensitive
Building or configuring guardrails - input and output filters, rate limits, logging - and testing whether they actually hold up rather than just assuming they do
Writing code, usually Python, to automate tests, build evaluation harnesses that run attack cases repeatedly, or wire security checks into the build-and-deploy pipeline
Sitting with data scientists and software engineers during design discussions to flag risks early, such as a plan to train a model on customer records without controls on what it can repeat back
Reviewing the supply chain around AI - pre-trained models pulled from public repositories, open-source libraries, third-party APIs - and checking for tampering or known vulnerabilities
Documenting findings for a mix of audiences: technical fixes for the engineering team, plain-English risk summaries for governance, compliance or audit colleagues

How graduates get in

Cyber security graduate schemes at large banks, consultancies, telecoms, defence contractors and technology firms - the most common structured route. You usually join as a general security graduate and specialise in AI once you are in, since few schemes recruit specifically for AI security at graduate level.
Direct entry as a junior security engineer, security analyst or penetration tester, then moving towards AI work as the employer's AI projects grow. This is very common - most people doing this job today arrived sideways rather than being hired into it.
Coming from the machine learning side: starting as a data scientist or ML engineer and moving into security. Realistic if you can demonstrate genuine security knowledge, and increasingly valued because deep ML understanding is scarcer among security people than vice versa.
Government and defence routes, including the intelligence and cyber agencies and defence suppliers. These typically require UK nationality and security clearance, and the vetting process can be slow.
A specialist MSc in cyber security, machine learning or AI, used as a conversion or deepening step. Useful but not a requirement, and it does not substitute for hands-on evidence.
Unusual but real: entering through a security research or bug-bounty background, where a visible track record of finding flaws - including in AI systems - carries weight with specialist firms. Degree apprenticeships in cyber security also exist and lead to the same place by a different path.

What employers ask for

A degree in computer science, software engineering, cyber security, maths, physics or a similar quantitative subject is the usual expectation. The subject matters more here than in many graduate roles because you need to be able to code and to reason about how models work - but strong self-taught evidence can substitute at smaller and specialist employers.
Typically a 2:1 or above for structured graduate schemes; smaller firms and specialist security consultancies often care far more about what you can demonstrate than about classification.
Practical programming ability, almost always Python, and comfort with Linux command line, APIs and cloud platforms. You will usually be tested on this at interview.
Demonstrable interest shown through something concrete - capture-the-flag (CTF) competitions, a home lab, security write-ups, contributions to open-source tooling, or experiments breaking and defending your own ML models. This counts for a lot.
Professional certifications are common but usually gained after you start rather than before; examples include foundational security certifications and offensive-security qualifications. Do not assume you need one to apply.
For defence, government and some financial roles: UK nationality and eligibility for security clearance, sometimes with residency requirements covering several preceding years.

Skills that matter

Understanding how machine learning models actually work

You cannot assess whether a model leaks training data or can be manipulated through its inputs unless you understand training, fine-tuning and inference well enough to reason about where the weak points are.

Adversarial thinking

The core of the job is imagining how a determined attacker would misuse a system in ways its builders never considered, then proving it rather than speculating.

Python and general software engineering

Most of your testing, tooling and automation is written in code, and you need to read other people's model and pipeline code to review it.

Cloud and infrastructure security fundamentals

Most real-world AI compromises come through ordinary weaknesses - exposed storage, over-permissive access, leaked API keys - rather than exotic attacks on the model itself.

Writing clearly for non-specialists

A finding only matters if a product manager or risk committee understands the consequence well enough to fund the fix, so reports and verbal explanations do real work here.

Comfort with ambiguity and fast-moving ground

Standards and attack techniques in this area change constantly, so you will often be assessing something with no established checklist and having to justify your own judgement.

Where it leads

Graduate or junior engineer: working under supervision, running defined tests, assisting on reviews, learning the organisation's systems. Typically the first one to two years, though graduate schemes with fixed rotations vary in length.
Security engineer or AI security engineer proper: owning assessments of specific systems, being the person product teams come to with questions, and starting to set standards rather than just applying them.
Senior or lead engineer: designing how AI security is done across the organisation, mentoring juniors, and influencing architecture before systems are built. Timeframes vary widely - some reach this in a few years, others considerably longer, depending on employer size and opportunity.
From here the paths split. Technical specialists go deeper into AI security research, red teaming or principal engineer roles. Others move towards management, into AI governance and risk (where the work becomes more about policy, regulation and assurance), or into consultancy and independent advisory work.
Longer term, routes include heading a security function, moving into AI safety or policy roles in government and research organisations, or founding or joining a specialist security startup - this area has an unusually high number of small firms.

What people get wrong

“It is mostly about using AI to do security - automated threat detection and so on.”

Those are two different jobs. Using AI as a security tool is one thing; securing AI systems themselves is another, and this title usually means the second. Some employers do blur them, so read the job description carefully - the word order in the title is not a reliable guide.

“The work is cutting-edge mathematical attacks on neural networks.”

A large share of the job is unglamorous and familiar: access controls, secrets management, logging, supply-chain checks on downloaded models and libraries, and persuading teams not to connect a chatbot directly to a production database. The novel model-level attacks are the interesting minority.

“You need a PhD in machine learning.”

A PhD helps for research-focused roles at AI labs, but most AI security engineering jobs are filled by people with a bachelor's degree and solid security engineering experience who have learned the ML side on the job. The scarcer combination is security depth plus enough ML to be credible.

“Graduates get hired straight into AI security roles.”

Dedicated graduate openings with this exact title are still relatively rare. Most entrants start in a broader cyber security or software role and specialise, so the practical plan is to target general security graduate routes and build AI-specific evidence alongside.

Where this varies

The job differs sharply by employer type. In banks, insurers and other regulated sectors, a lot of the work is assurance and governance - documenting controls, satisfying internal audit and regulators, and reviewing models before they go live. At technology companies and AI labs it is far more hands-on engineering and research, closer to building tooling and red-teaming models. In consultancies you rotate across many clients, giving breadth but less depth, and client-facing communication matters more. Defence and government work is often the most technically interesting but comes with clearance requirements, nationality restrictions and limits on what you can discuss or put on a CV. Geographically, London has the largest concentration of financial and consultancy roles, with significant clusters in Cambridge, Bristol, Manchester, Edinburgh and Cheltenham. Because the field is new, job titles are inconsistent - similar work appears as 'AI red teamer', 'ML security engineer', 'AI assurance engineer' or simply 'security engineer' on an AI platform team, so search broadly.

General guidance about the role across the UK market, not about any specific employer. Entry routes and requirements vary — always check the individual job advert.