5 best practices to secure AI systems

Host A: Welcome back to AI Catchup Weekly, I'm here with my co-host, and today we're diving into something that affects pretty much every organization that's either using or thinking about using AI — and that's how to actually keep those systems secure.

Host B: Right, and this isn't just theoretical anymore. AI is embedded in critical operations across industries, which means the attack surface has grown in ways that traditional security tools honestly weren't built to handle.

Host A: Exactly. So let's walk through the five foundational practices that security teams really need to have in place. The first one sounds familiar but it's more nuanced with AI — strict access control and data governance. We're talking role-based permissions, encryption of models and training data, the whole works.

Host B: That last part is interesting though — encrypting the actual AI models themselves. Most people think about protecting data, but the model is essentially a valuable asset too, right? Leaving it unencrypted on a shared server is basically rolling out the welcome mat for attackers.

Host A: Great way to put it. Now the second practice gets into AI-specific threats, and this is where things get really unique. Prompt injection is sitting at the top of OWASP's vulnerability list for large language models — that's when someone sneaks malicious instructions into an input to hijack the model's behavior.

Host B: Which is wild when you think about it — you're not attacking the infrastructure directly, you're essentially tricking the AI itself. So how do teams defend against something like that?

Host A: AI-specific firewalls that sanitize inputs before they ever reach the model, and regular adversarial testing — basically ethical hacking but for AI. Red team exercises that simulate things like data poisoning and model inversion attacks. The key point is this testing needs to be baked into the development lifecycle, not added as an afterthought.

Host B: That "baked in not bolted on" idea is huge, and honestly applies to security in general. What about visibility? Because these AI environments span cloud, on-premise, endpoints — it feels like keeping track of all that could be a nightmare.

Host A: That's practice number three — unified ecosystem visibility. When your security data is siloed across all those different environments, attackers can move through the gaps completely undetected. You need a single view where telemetry from network monitoring, cloud security, identity management and endpoints all feeds together so analysts can connect the dots.

Host B: Because an anomalous login, a lateral movement attempt, and a data exfiltration event each look minor in isolation — but together they're telling a very serious story.

Host A: Precisely. And that connects directly to practice four — continuous monitoring. AI systems aren't static, models get updated, data pipelines change, user behavior shifts. Rule-based detection tools just can't keep up because they're looking for known attack signatures, not real-time behavioral changes.

Host B: So you need tools that actually learn what "normal" looks like and flag deviations as they happen. That's the kind of thing that catches those slow, low-and-slow attacks that might otherwise fly under the radar for weeks.

Host A: And when something does slip through — because let's be honest, with AI incidents it's a matter of when not if — that's where practice five comes in: a clear incident response plan. And for AI, recovery isn't just patching a server. You might need to retrain a model that was fed corrupted data, or audit everything the system produced while it was compromised.

Host B: That's a detail a lot of organizations probably haven't thought through yet. Planning for that in advance versus scrambling under pressure — the difference in reputational and operational damage could be massive.

Host A: The article also highlights three providers leading the way in AI security tooling — Darktrace with its self-learning AI, Vectra AI for hybrid and multi-cloud environments, and CrowdStrike's Falcon platform for endpoint security. Each brings something distinct depending on your architecture.

Host B: So whether you're just starting to build out your AI security strategy or tightening up an existing one, these five practices give you a solid framework to work from. Lots to think about here.

Host A: Absolutely. That's going to do it for today's episode of AI Catchup Weekly — thanks so much for tuning in. Stay curious, stay secure, and we'll see you next week.

Host B: Take care everyone — and maybe go check if your AI models are encrypted. Just saying.