Understanding AI Security: Definitions, Risks, and Best Practices
Artificial intelligence now sits inside the tools businesses use to run and to defend themselves, and that dual role makes it a target. Protecting it has become its own discipline. IBM’s 2025 Cost of a Data Breach Report found that 13% of organizations had already suffered a breach of an AI model or application, and 97% of those lacked basic AI access controls. Adoption, in other words, is outpacing protection. This guide gives security teams a grounded overview: a working definition, the major risk categories, and the practical steps that actually move the needle. The result of all this change is a widening gap between how much AI organizations run and how much of it they can actually see and secure. Security teams that once treated AI as someone else’s problem are now writing it into their charters.
Key Takeaways
- AI security protects AI systems plus the data they use, and it also uses AI to strengthen defense.
- IBM found that 97% of organizations breached through AI had no proper access controls.
- Risks fall into four groups: model attacks, data risks, supply-chain flaws, and governance gaps.
- Prompt injection remains the number-one risk in OWASP’s ranking for LLM applications.
- Governance, least-privilege controls, and continuous monitoring are the highest-value practices.
AI Security, Defined
For teams new to the topic, the first question is: what is AI security? At its core, it is the practice of protecting artificial intelligence systems, their data, models, and pipelines, from attack, while governing how AI is used so sensitive information stays controlled.
The discipline has two faces. Security for AI defends the models and data you build or buy. AI for security turns machine learning loose on your own defenses, powering faster AI-driven threat detection and response. A complete program needs both, because a sharp detection engine offers little protection if the model behind it can be poisoned or stolen. Treating the two as one effort, rather than separate projects, keeps a blind spot from opening between them. In practice, most organizations find they are already doing pieces of each without a plan tying them together. Writing that plan down is often the first real step toward maturity.
Why AI Security Matters More Than Ever
Two forces are pushing this up the priority list. The first is the pace of adoption, with AI wired into products faster than controls can follow. The second is that attackers now wield AI as well.
Attackers lean on AI mostly to make phishing and deepfakes more convincing.
In 2025, roughly one in six breaches involved attackers using AI, most often to craft convincing phishing or deep fakes. Meanwhile, unsanctioned tools, known as shadow AI, quietly move sensitive data outside any policy. For a fuller picture of how both sides are evolving, the 2026 AI cybersecurity landscape is shifting quickly on offense and defense alike. The takeaway for security teams is that AI raises the stakes on both sides of the fence: it expands what defenders can do and sharpens what attackers can attempt. Shadow AI is the quiet version of that problem, as employees reach for helpful tools faster than policy can keep up, moving sensitive data in the process. That single dynamic, well-meaning staff paired with ungoverned tools, sits behind a large share of AI incidents.
The Main Categories of AI Security Risk
AI risks are far easier to manage once grouped. Four categories cover most of what security teams actually face.
Sorting risks into groups turns a sprawling problem into a checklist.
| Category | Example risks | Why it is hard |
|---|---|---|
| Attacks on models | Prompt injection, jailbreaks, model theft | The model cannot always tell data from instructions |
| Data risks | Poisoning, sensitive data leakage, shadow AI | Data is the attack surface, and it moves constantly |
| Supply chain and tooling | Compromised plugins, insecure vector stores, over-broad agents | Trust is inherited from parts you did not build |
| Governance and misuse | Weak oversight, bias, misinformation | Failures are organizational, not only technical |
| Warning: prompt injection is the standout. It tops the OWASP Top 10 for LLMs because a model often cannot separate a trusted instruction from malicious text hidden in the data it reads. |
For the definitive, ranked breakdown, security teams turn to the ten most critical LLM risks, updated to reflect real-world incidents. Weighing the risks that come with AI against its benefits is now a routine part of any security review. Grouping risks this way also clarifies ownership: model and data risks usually fall to engineering and data teams, while supply-chain and governance issues pull in procurement, legal, and leadership. Naming who owns each category is half the battle, and it maps each risk to a specific defense in the practices that follow.
Best Practices for Securing AI
No single control is enough, so defense in depth is the rule. These practices, drawn from established guidance, form a workable baseline.
| Practice | What it protects |
|---|---|
| Inventory every AI system and tool | Visibility, including unsanctioned shadow AI |
| Enforce least-privilege access | Models, data, and agents treated as identities |
| Validate inputs and outputs | Guards against prompt injection and leakage |
| Protect the data pipeline | Training data, live prompts, and stored records |
| Red-team models regularly | Surfaces weaknesses before attackers find them |
| Log and monitor activity | Early detection and a usable audit trail |
| Vet AI vendors and components | Contains supply-chain risk |
| Key stat: the payoff is real. Organizations that use AI extensively in their own defenses save close to 1.9 million dollars per breach and contain incidents about 80 days faster, according to IBM. |
Many of these are familiar habits pointed at a new surface. The same discipline behind proven endpoint security habits carries directly over to the data, models, and agents inside an AI system.
“The goal is the secure development, deployment, and governance of generative AI systems.” OWASP GenAI Security Project
[Video: “A cybersecurity expert’s guide to securing AI products with Google SAIF” by Google Cloud Tech: https://www.youtube.com/watch?v=2xSNyfAbWb8]
This short explainer walks through securing AI products end to end, from the data they learn on to the way they are deployed. The unifying idea is defense in depth: assume any single control can fail, then layer others behind it. That mindset, more than any single tool, separates resilient AI programs from fragile ones, so it is worth revisiting the practices on a schedule as threats and tooling shift. Small, regular reviews beat a single big push that ages out within months.
Frameworks and What Comes Next
Nobody has to start from scratch. Several frameworks give teams a shared language: the NIST AI Risk Management Framework for governance, the OWASP list for application risks, MITRE ATLAS for adversary techniques, and Google’s secure-AI framework for engineering controls. The ISO 42001 standard adds a certifiable management system on top.
What comes next is more autonomy. As AI agents begin taking actions on their own, from sending emails to moving money, the attack surface widens and oversight matters more. The data and governance questions AI raises will only sharpen, which is why building security in now, rather than later, is the safer bet. In practical terms, that means folding AI into the risk reviews a team already runs, then extending them to cover models, agents, and the data they touch. The frameworks above are less competing choices than pieces that fit together, and most mature programs draw on several at once.
Frequently Asked Questions
What is AI security?
It means protecting AI systems, including their data and models, from attack, and governing how the technology is used. It also covers using AI to strengthen an organization’s broader defenses.
What are the biggest AI security risks?
Prompt injection, sensitive data leakage, data poisoning, weak supply-chain controls, and excessive agent permissions lead most lists. They exploit how models learn and act, which older tools were never built to catch.
What is prompt injection?
It is an attack where hidden or crafted input makes a model ignore its intended instructions, then leak data or take unauthorized actions. It ranks first on OWASP’s LLM Top 10.
How do I start securing AI?
Begin with an inventory of every AI tool in use, then apply least-privilege access plus monitoring. Since most AI breaches involve missing access controls, visibility and access deliver the fastest gains.
Which frameworks should we follow?
Common choices are the NIST AI RMF, OWASP’s Top 10 for LLMs, and MITRE ATLAS. Most teams combine them based on whether the priority is governance, engineering, or threat modeling.
AI Security Is Core Work Now
AI security is no longer a corner of the field; it is fast becoming central to every security team’s work. The definitions are settling, the risks are increasingly well mapped, and the best practices are largely familiar disciplines aimed at a new kind of system. Start with a clear definition, group the risks so they feel manageable, lock down access, and lean on proven frameworks. None of it requires reinventing security, only extending it to where AI now lives. The teams that treat this as core work, not an afterthought, are the ones that get to use AI with confidence.
