If your company has shipped a chatbot, a support agent, or any product feature built on a large language model, someone on your team has probably already asked the question: is this thing safe to put in front of customers? That question is what AI and LLM security is trying to answer. It is a newer discipline than most of information security, and the terminology is still settling, so it is worth defining plainly before you decide whether you need it and who should do it.
What AI and LLM security actually means
AI security is the practice of finding and fixing the ways an AI system can be manipulated, tricked, or misused, either by an outside attacker or by an unexpected input from a normal user. LLM security is the part of that practice focused specifically on large language models, the technology behind tools like customer support bots, internal copilots, and AI-powered search.
It sits next to your existing security program rather than replacing it. Your firewall, your access controls, and your SOC 2 controls still matter. AI security adds a layer that deals with problems traditional security testing was never built to catch, because the vulnerability lives in the model's behaviour, not in a server misconfiguration.
What the testing actually involves
In practice, testing your own AI means probing it the way an attacker would, then documenting what broke and how to fix it. A few categories come up constantly:
- Prompt injection. An attacker hides instructions inside text the model reads, a support ticket, a PDF, a webpage, and the model follows those instructions instead of yours. This is the AI equivalent of SQL injection, and it is the most common finding in real assessments.
- RAG leakage. Many LLM products use retrieval-augmented generation to pull from a private knowledge base before answering. If access controls on that data are weak, a user can coax the model into revealing documents, records, or other customers' data it should never surface.
- Agent tool abuse. AI agents that can send emails, query databases, or call internal APIs are only as safe as the guardrails around those actions. If an agent can be tricked into calling a tool it shouldn't, the damage is no longer confined to a bad chat response.
- OWASP LLM Top 10 coverage. The OWASP Foundation maintains a standard list of the most common LLM vulnerabilities, covering everything above plus issues like training data poisoning, excessive agency, and insecure output handling. A proper assessment works through that list systematically rather than testing a handful of obvious cases and calling it done.
A real assessment produces the same kind of deliverable a penetration test does: a report ranking findings by severity, proof-of-concept examples showing how each issue was triggered, and concrete remediation guidance your engineering team can act on. If you want the specifics of how that work is scoped, our AI security assessments page walks through what is tested and how engagements are structured.
Who actually needs this
You need AI security testing if any of the following is true: you have shipped or are about to ship a customer-facing chatbot or AI feature, you have deployed an internal AI agent with access to real systems or data, a customer or prospect has asked how you secure your AI product, or you are pursuing a certification like SOC 2 or ISO 42001 that now expects AI risk to be addressed explicitly. That last point catches a lot of companies off guard. AI security used to be optional. It is increasingly becoming an expectation baked into procurement questionnaires and compliance frameworks, particularly for B2B SaaS companies selling into enterprise or regulated buyers.
Realistic timelines
A focused AI security assessment on a single product or feature typically runs a few weeks from kickoff to final report, not months. The variables that move the timeline are the number of AI-powered features in scope, how many tools or integrations an agent has access to, and how quickly your team can provide test access. Companies sometimes assume this is a multi-month audit on the scale of a full compliance program. It usually isn't. It is closer in scope and duration to a targeted penetration test. If AI risk is one part of a broader governance effort, for example building toward ISO 42001, that timeline extends because you are standing up policies and processes alongside the technical testing. Our ISO 42001 readiness work covers that broader governance piece if your goal is a management system for AI, not just a point-in-time test.
Common misconceptions
A few assumptions come up repeatedly and are worth correcting directly.
"Our AI vendor already secured this." Vendors like OpenAI, Anthropic, and Google secure the underlying model. They cannot secure how you configured it, what data you connected it to, or what tools you gave it permission to call. That configuration is your responsibility, and it is where most real-world incidents originate.
"We tested it with a few obvious prompts and it held up." Prompt injection techniques evolve constantly, and a model that resists an obvious attempt often falls to a more creative one. Ad hoc testing by someone without dedicated AI security experience tends to miss the findings that matter most.
"This is just a chatbot, the stakes are low." If the chatbot can access customer records, internal documents, or backend tools, the stakes are identical to any other system with that access. The interface being conversational doesn't lower the risk.
"We'll deal with this after launch." Retrofitting security into a shipped product is more expensive and more disruptive than testing before launch, especially once real users and real data are flowing through it.
Where to start
If you are unsure whether your AI deployment needs a dedicated assessment or fits under a broader security review, the honest answer depends on what the system has access to and who is using it. The fastest way to get a straight answer is to walk through your setup with someone who does this work regularly.
Talk to us about your AI deployment and we'll tell you plainly what level of testing makes sense, no upsell. Get in touch to start the conversation.