Hiring an AI cyber security consultancy? What to ask (and avoid)

Right, so you’ve been tasked with securing your company’s foray into AI. Maybe you’re a leader trying to make sense of the wild west of new tools, or perhaps you’re one of the poor practitioners who’s suddenly become “the AI person” by default. Or, worse still, you’re in IT or security, frantically trying to put a lid on the Pandora’s Box of ungoverned AI tools your teams are already using. You’ve realised this isn’t just another IT project; it’s a beast with its own rules. And now, you’re thinking, “Perhaps we need outside help. An AI security consultant.” Let me tell you, that’s a sensible thought. But just like everything else in this ‘messy middle’ of AI adoption, finding the right help is a minefield. I’ve been in the room when it’s gone spectacularly wrong, so let’s cut through the noise and figure out what you actually need to ask—and what to absolutely avoid—when you’re looking for someone to secure your AI initiatives.

The new wilderness: why AI security isn’t just ‘more cyber’

Let’s get one thing straight: if you think traditional cyber security expertise is enough for AI, you’re in for a rude awakening. It’s not. It simply isn’t. I’ve seen too many organisations assume their existing security team or a general cyber security consultancy can just ‘bolt on’ AI security. They can’t, not without a significant re-tooling of their mindset and skillset.

AI introduces an entirely new set of attack vectors and vulnerabilities that traditional cyber security wasn’t designed to handle. We’re talking about things that make your usual firewall rules look positively quaint:

Data Poisoning: Imagine an attacker subtly corrupting the training data for your AI model, leading it to make biased or incorrect decisions, or even to deny service. This isn’t a SQL injection; it’s a fundamental attack on the integrity of your AI’s brain.
Model Evasion: An attacker crafts inputs specifically designed to bypass your AI’s detection or classification, making it misidentify malicious content or approve fraudulent transactions. Your AI thinks everything’s fine, whilst you’re being royally mugged.
Prompt Injection: For large language models (LLMs), this is the big one. Attackers manipulate the inputs to force the model to reveal sensitive information, bypass safety filters, or execute unintended actions. It’s like whispering a secret command into your AI’s ear that it can’t refuse.
Credential Sprawl and Supply Chain Risks: Every new AI tool, every new API, every new data source you hook up to your models introduces more credentials, more access points, and more potential for compromise. The supply chain for AI models and data is often opaque and sprawling, making it a nightmare to secure.

These aren’t just theoretical threats; they’re happening now. Your general cyber security consultancy, whilst perfectly capable of securing your network or patching your servers, might frankly be out of their depth when confronted with the nuances of MLOps security or the ethical implications of model bias in a security context. They simply haven’t ‘been in the room’ when these specific AI-related incidents have unfolded.

The siren song of the ‘ai security expert’: cutting through the noise

The moment AI became the hot topic, every consultancy worth its salt (and many that weren’t) suddenly re-badged itself as an ‘AI security expert’. It’s like watching a gold rush; everyone’s digging, but most are just kicking up dust. This market is rife with opportunists, and it’s your job to separate the wheat from the chaff. You need someone who’s genuinely been in the trenches, not just someone who’s read a few whitepapers.

What to ask: unearthing genuine expertise

When you’re interviewing potential consultants, you need to be ruthless. Don’t be afraid to dig deep and challenge their assumptions. Here’s what I’d be asking, in no particular order of importance:

”Show Me, Don’t Tell Me” - Practical Experience:
- ”Can you give me specific examples of how you’ve secured an AI model against data poisoning or model evasion attacks?” Don’t accept theoretical answers. Ask for real-world scenarios, even if anonymised. Look for details on mitigation strategies, not just identification.
- ”Describe a time you had to implement security controls within an MLOps pipeline. What tools did you use, and what were the biggest challenges?” This probes their understanding of the operational side of AI, not just the conceptual. Anyone can talk about ‘secure development’; you need someone who’s actually done it.
- ”How do you approach securing Generative AI/LLM applications, specifically against prompt injection or data leakage?” This is a critical and rapidly evolving area. Look for current, practical understanding beyond basic prompt engineering.
Beyond the Firewall—MLOps and Data Governance:
- ”How do you assess and mitigate risks related to the data supply chain for AI models?” This goes beyond network security. It’s about data provenance, third-party model risks, and intellectual property protection.
- ”What’s your methodology for auditing AI models for bias or fairness, and how does that integrate with security considerations?” Ethical AI and secure AI are inextricably linked. A good consultant understands this intersection.
- ”How do you ensure data privacy within AI models, especially when dealing with sensitive information or differential privacy techniques?” This is about data in use and in training, not just data at rest or in transit.
The ‘How’ Not Just the ‘What’ - Methodology and Integration:
- ”How do you integrate your security recommendations into our existing development and operational workflows?” You don’t want a report that sits on a shelf. You need actionable advice that fits your context.
- ”What’s your approach to building internal capability? Will you just fix things, or will you help us learn to do it ourselves?” The best consultants empower you, not create dependency. They should be working themselves out of a job.

Red flags and what to avoid: spotting the imposters

Equally important is knowing when to walk away. Here are some glaring red flags:

The Buzzword Bingo Brigade: If their pitch is full of abstract terms like “synergistic AI-driven threat intelligence platforms” without ever explaining how that actually solves your problem, run a mile. Substance over jargon, always.
One-Size-Fits-All Solutions: AI security is highly contextual. If they’re trying to sell you a pre-packaged solution without a deep dive into your specific models, data, and use cases, they’re not listening, or worse, they don’t know enough to customise.
Ethics Without the Engineering: Many consultancies now offer ‘AI ethics’ services. Whilst crucial, if they focus solely on high-level ethical frameworks without demonstrating a robust technical understanding of how to implement security controls that underpin those ethics, they’re missing the point. Ethics without security is just wishful thinking.
Lack of Specific AI Case Studies: Ask for examples. If all they can offer are generic cyber security success stories, or they’re cagey about any AI-specific work, it’s a huge red flag. They might be good at general cyber, but they’re probably just learning on your dime when it comes to AI.
Overpromising and Under-delivering: If they claim to solve all your AI security woes with a single, quick engagement, they’re either naive or dishonest. AI security is an ongoing journey, not a destination.

Defining success: what does a good outcome look like?

Before you even sign a contract, you need to define what success looks like. This isn’t just about getting a report. It’s about tangible outcomes:

Clear, actionable recommendations: Not just ‘improve security’, but ‘implement X control on Y model using Z tool by date A’.
Risk reduction metrics: Can they show how their work has demonstrably reduced specific AI-related risks?
Improved internal processes: Did their engagement lead to better MLOps security practices, clearer data governance for AI, or a more secure AI development lifecycle?
Enhanced internal capability: Are your teams now better equipped to handle AI security themselves?

Integrating the insights: from report to reality

A consultant’s report is only as good as its implementation. A truly effective AI security consultant won’t just deliver a document; they’ll help you integrate their findings into your broader AI governance and adoption strategy. They’ll understand that AI security isn’t a siloed activity but an integral part of your overall AI journey.

Their work should directly inform your internal AI security checklist, helping you build a robust, repeatable process for securing future AI initiatives. It’s about moving from reactive fire-fighting to proactive, integrated security.

So, when you’re looking for that crucial external expertise, remember: you’re not just buying a service; you’re investing in your organisation’s future. Be discerning, be demanding, and don’t settle for anything less than genuine, practical experience. Your AI future, and your security posture, depend on it.