What Is AI Security? A Practical Definition for Security Teams

What Is AI Security? Two Classic Meanings and a Third Most Guides Miss

Your developers asked an AI assistant for code this morning, and it shipped by lunch. AI security is the practice of protecting AI systems, using AI to defend other systems, and securing the code AI writes.

That code carries its own risks, and those risks belong under the AI security umbrella too.

So it helps to hold three ideas at once. Two are classic. One is newer, and it is the part most guides skip.

What are the three meanings of AI security?

The first meaning is about protecting AI systems. The second is about using AI to protect other things. We go deeper into exploring AI-generated code after the table.

What does securing the code AI writes mean?

AI now writes a large share of the code that ships to production, and that code can carry bugs, weak logic, and risky dependencies. A dependency is outside code your software pulls in to do a job, like a library or package.

When an AI assistant suggests code, it can also suggest flawed patterns and unsafe packages. If you want a fuller list, see the common vulnerabilities in AI-generated code.

This third meaning is where a lot of real risk lives today. It is also where this guide spends the most time.

Why does AI security matter now?

AI adoption moved fast, and the risk picture moved with it. The World Economic Forum's Global Cybersecurity Outlook put a number on it.

87% of surveyed cyber and business leaders identified AI-related vulnerabilities as the fastest-growing cyber risk over the course of 2025.

Data exposure is part of the story too. According to the 2026 Verizon DBIR (Data Breach Investigations Report), source code is the #1 data type leaked to unauthorized GenAI tools.

Put together, these numbers show why AI security is a current problem, not a future one. Teams are already generating code, sharing data with AI tools, and shipping the results.

What are the main AI security risks?

The risks fall into two groups. One group targets AI systems themselves. The other shows up in the code AI helps produce.

A useful reference for the model side is the OWASP Top 10 for LLM Applications, from the Open Worldwide Application Security Project. It lists common weaknesses in large language model apps and gives teams a shared vocabulary.

What risks target AI models and data?

This is the securing-AI half of the topic. These threats target the model, its data, or the systems around it.

• Data poisoning: An attacker corrupts training data so the model learns the wrong things or misbehaves on command.
• Prompt injection: Hidden or crafted instructions trick a model into ignoring its rules or leaking data.
• Adversarial attacks: Small, tuned inputs fool a model into making wrong predictions.
• Model theft: Someone copies or extracts a model's weights or logic without permission.
• Shadow AI: Employees use AI tools that no one approved or tracks, which creates blind spots.

These risks are real, and they need model-layer and runtime controls. That work sits with teams and tools focused on the model itself, not on the code layer this guide centers on.

What risks come with AI-generated code?

Now the newer half. When AI writes code, it can introduce weak code and risky dependencies at speed.

The benchmark BaxBench shows AI-generated backends are often insecure: 62% of solutions generated by even the best model are either incorrect or contain a security vulnerability.

Dependencies are a second problem. AI assistants sometimes suggest packages that do not exist, known as a hallucinated dependency.

Our research found that 34% of AI-suggested dependency versions are hallucinated, meaning they don't exist in real package registries. We also found that only about 1 in 5 AI-suggested dependency versions are safe to use, so 80% carry risk.

• Key point: Attackers can register a hallucinated package name and wait for AI tools to recommend it, turning a made-up name into malware.
• Key point: Risky and outdated dependencies expand your attack surface long after the merge.

You can dig into the data in our State of Dependency Management 2025. For the wider context, see our guide to software supply chain security.

What are AI security best practices?

Good AI security is not one control. It is a few habits that work together across governance, code, and prioritization.

The steps below move from policy to practice. Start with knowing what you have, then secure the code, then focus on what matters.

How do you govern the AI tools your teams use?

You cannot secure what you cannot see. Start with an inventory of the AI tools, models, and assistants your teams use.

• Key point: List every AI tool in use, including the unofficial ones, to close shadow AI gaps.
• Key point: Set clear rules for what data can go into AI tools and who approves new ones.
• Key point: Adopt a shared framework so your controls map to known risks, like the OWASP list above or the NIST AI Risk Management Framework.

A framework gives you a checklist and a common language. It also makes audits and reviews far less painful.

How do you secure AI-generated code before it ships?

To catch problems in AI-written code before they reach production, two techniques do most of the work:

• SAST, or static application security testing, scans your own source code for flaws without running it.
• SCA, or software composition analysis, checks the open source dependencies your code pulls in.

Endor Labs runs AI static analysis that finds these code-level flaws and needs no hand-written rules to do it. It also offers AI code security review to check changes as they land.

• Key point: Scan AI-generated code the same way you scan human code, since both can introduce flaws.
• Key point: Review dependency suggestions before you accept them, so hallucinated and risky packages never reach main.

How do you decide which findings to fix first?

Scanners find a lot. Most findings are not worth an interrupt, and chasing all of them burns time.

The fix is reachability analysis. Reachability analysis traces whether your code can reach and run a flaw, so you fix the risks that matter first.

Endor Labs builds a call graph across your application and applies full-stack reachability analysis to confirm what is really exploitable. Full-stack reachability can cut vulnerability noise by up to 92%.

How does Endor Labs secure the code AI writes?

Endor Labs focuses on the code layer. It is the security intelligence layer for agentic software development, built to secure the code AI writes and the dependencies it pulls in.

The pressure here is real. Faros research found the incidents-to-PR ratio is up 242.7% with high AI adoption, which more than tripled the incident rate.

AURI, agentic application security platform answers that pressure with evidence, not more alerts. It runs AI SAST with no rules to write, SCA with reachability, plus secrets and container scanning in one place.

Its context-aware prioritization uses full-stack reachability to show which findings can truly be reached and run. Developers spend time on real risk, and fixes ship faster.

• Key point: Endor Labs covers the code layer: AI SAST, AI Code Security Review, SCA with reachability, and secrets and container scanning.
• Key point: Model-layer and runtime threats need separate controls; Endor Labs concentrates on the code and dependencies AI produces.

How do you get started with AI security?

You do not need to solve everything at once. Start with the layer that is growing fastest, which is the code your AI tools write.

Here is a practical sequence:

• Inventory your AI: List the AI tools and assistants in use, and set rules for data and approvals.
• Scan AI-written code: Apply SAST and SCA to AI output the same way you do for human code.
• Check dependencies: Reject hallucinated and risky package suggestions before they merge.
• Prioritize by reachability: Fix what is exploitable first, and cut the rest of the noise.
• Adopt a framework: Map your controls to a known standard so coverage stays clear.

Work through these steps and you will secure both the AI you use and the code it produces. Book a demo to see how Endor Labs secures the code AI writes.

Frequently Asked Questions

What is AI security in simple terms?

AI security means protecting AI systems from attack, using AI to defend other systems, and securing the code that AI tools write. It covers both the AI you build and the AI-generated code you ship.

How is AI security different from AI for cybersecurity?

AI security is the broad practice of keeping AI and AI-written code safe. AI for cybersecurity is one part of it that uses AI to run defenses.

Is AI-generated code safe to use?

Not by default, since AI tools can produce insecure code and risky dependencies. Scan and review AI-written code before it ships, the same way you check human code.

What is a hallucinated dependency?

A hallucinated dependency is a package an AI tool suggests that does not exist in real package registries. Our research found that 34% of AI-suggested dependency versions are hallucinated.

What is reachability analysis?

Reachability analysis traces whether a vulnerability can be reached and run through your code paths. It lets you fix exploitable risks first and set aside findings that cannot be triggered.

Which framework should I use for AI security?

The OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework are both strong starting points. They give you a shared checklist and a common language for AI risk.