Imagine a security camera that doesn't just capture break-ins—it sees them coming and alerts you in time to act. That's what AI is bringing to cybersecurity. It detects irregularities faster and identifies suspicious behavior more effectively, making it possible to anticipate attacks early enough to stop them.
But ironically, the same technology that’s redefining cybersecurity also introduces new vulnerabilities that are unfamiliar, fast-moving, and easy to miss. And many security teams aren’t equipped to protect against them. It’s the catch-22 of the 21st century: The tools that make you stronger also expose you to new risks.
In this guide, we’ll explain what AI security is and where threats are emerging. We’ll also highlight how AI can strengthen your defenses and share best practices for keeping AI security systems in check.
What Is AI Security?
AI security refers to two things: the use of artificial intelligence to protect digital systems, and the measures that safeguard AI itself from misuse or attack. To understand the role of AI in cybersecurity, it helps to see how both sides work—and why they matter.
The first category uses AI cybersecurity tools to defend digital systems. They scan massive volumes of telemetry in real time, learning and adapting as new patterns emerge. In fast-changing environments where static detection rules fall short, AI spots anomalies, prioritizes threats, and accelerates response.
The second category focuses on artificial intelligence protection, ensuring that the AI models, prompts, training data, and infrastructure don’t become backdoors for attackers. This is especially critical in tools like AI-powered code generators, where a single manipulated input can trigger dangerous results.
The bottom line? Artificial intelligence security isn’t one-dimensional. It’s not just about faster threat detection—it’s also about safeguarding the systems doing the detecting. When attackers compromise your models’ logic, behavior, or training data, the integrity of your entire security posture is at risk.
Why Is AI Security Important?
AI has come a long way in a short time. What was once novel tech now powers everything from cybersecurity tools and fraud detection systems to customer workflows and enterprise automation. But that reach brings risk. Flawed or compromised AI can misread threats, leak sensitive data, or cause massive failures. Because of this, modern organizations treat AI security as a necessity, not a perk.
As AI adoption grows, so does the incentive for attackers. From financial institutions to healthcare providers, more companies are relying on AI to process sensitive data and automate responses. That makes AI systems prime targets for adversaries looking to manipulate models, poison training data, or reverse-engineer outputs. And the more embedded in critical operations AI becomes, the greater the potential impact of a breach.
In 2024, global cybercrime losses exceeded $16 billion, a 33% spike from 2023. But only one in five companies consider themselves ready to defend against AI-powered bot attacks, and over half say generative AI has made threats harder to detect and stop. As attackers move faster with automation, defenders need safeguards that keep pace—or stay ahead.
AI security also affects how safely teams can innovate. If an AI model learns from flawed or manipulated data, it can produce unreliable results or reinforce hidden bias. That erodes trust in the system and slows down adoption. And as these models shape more systems, from automation to analytics, even small vulnerabilities can spiral into bigger problems, making teams more cautious about using AI in the first place.
Risks and Challenges to AI Systems
The more capable AI becomes, the more creative attackers get. Here are some of the most pressing challenges security teams face when protecting AI.
Data Security Risks
AI models thrive on data, but that dependence introduces risk. Training and inference datasets often contain sensitive or proprietary information, making them easy targets. If attackers extract or poison that data, they can compromise not just the model but the output it generates.
Effective artificial intelligence data security requires more than encryption—it means controlling access, validating inputs, and watching for leakage throughout the model’s lifecycle, from development to real-world use.
Complexity of AI Algorithms
Even seasoned engineers sometimes struggle to decode the layers that make AI work. Because of their complexity, teams find it difficult to trace decisions or audit for flaws. In opaque, black-box models, the lack of transparency makes it even harder to spot vulnerabilities as they emerge.
Adversarial Attacks
Attackers don’t always go after code—sometimes, they target the model’s confidence. By subtly altering input data, they make AI produce wildly incorrect results. Adversarial examples—inputs designed to exploit how models interpret inputs—can cause a facial recognition system to misidentify someone or a spam filter to miss a malicious email. Prompt injection in generative AI works similarly, manipulating responses or leaking sensitive data.
Model Theft and Reverse Engineering
Attackers can steal or reverse-engineer AI models to expose intellectual property or undermine security. Once they extract the model’s architecture or weights, they can retrain it with poisoned data or strip out proprietary logic. This opens the door to industrial espionage, deepfakes, or malicious repurposing of your own tech.
Supply Chain Vulnerabilities
AI systems rarely operate in isolation. They rely on third-party components, libraries, and APIs, often from unverified sources. A single compromised dependency can grant widespread access. These attacks are difficult to detect, especially when the vulnerable code is buried deep in machine learning pipelines. Many flaws stem from rushed or incomplete vetting during development, where AI risks slip through unnoticed.
Bias, Drift, and Model Decay
AI models aren’t static—they shift over time. Data drift, skewed inputs, and changing user behavior can gradually steer a model off course, giving attackers a chance to bypass defenses. Without regular monitoring and retraining, small degradations can snowball into major security gaps and biased outputs.
How AI Improves Cybersecurity: Benefits Explained
Cybersecurity threats move fast, and AI helps teams keep up. From smarter detection to faster response, here’s how it can strengthen your security stack:
- Faster threat detection: AI scans massive volumes of network activity to flag anomalies in real time, catching threats that human teams might miss.
- Less alert fatigue: By filtering out false positives and prioritizing real risks, AI allows security teams to focus on what matters.
- Stronger endpoint protection: AI tools monitor devices for suspicious behavior, spotting unfamiliar malware or abnormal access patterns.
- Smarter authentication: Behavioral analytics and adaptive authentication let AI go beyond passwords to adjust access controls based on real-time user activity.
- Automated response: AI isolates compromised systems, disables affected accounts, triggers alerts, and recommends next steps like patching or forensic analysis, reducing response time and damage.
- Sharper decision-making: AI connects the dots between events and data sources, giving security teams clearer context and earlier warnings.
Best Practices for AI Security
AI security only works when it’s built in from the start. These best practices can help you design, deploy, and maintain AI systems with security in mind:
- Use secure, high-quality data: Train models on reliable, vetted data to reduce the risk of bias, poisoning, and poor performance.
- Apply formal data governance: Implement access controls, logging, and encryption across the AI lifecycle—from ingestion to inference.
- Integrate AI with existing tools: Connect AI systems to your broader security infrastructure, like SIEMs or threat intelligence feeds, for real-time detection and response. AI can also reduce false positives in secret scanners and alerting tools.
- Build transparency into AI systems: Document model behavior and data sources so your security team can understand and audit decisions better.
- Embed security from development through deployment: Use secure coding practices and threat modeling, and run regular vulnerability scans throughout the SDLC.
- Continuously monitor and retrain models: Watch for drift or misuse, and retrain models as data or risk evolves.
- Enforce access policies across the AI supply chain: Vet third-party libraries and APIs before integration, and tightly control what AI systems can access or communicate with.
- Align with formal frameworks: Follow established security standards like the NIST AI Risk Management Framework or ISO/IEC 23894 to ensure consistency, accountability, and transparency in your AI systems.
Protect Your AI Systems With Legit Security
AI is everywhere in modern software development, but that's not always a good thing. Without the right controls, it’s easy for things like unreviewed AI-generated code, unmanaged models, and risky plugins to quietly sneak past static checks and into production undetected.
Legit Security helps your security team stay a step ahead of threats. It tracks where and how AI is at work across your software lifecycle, from prompt to production, without slowing down delivery. With Legit’s new AI Discovery capabilities, you can enforce policy, reduce risk, and keep evolving AI use in check.
Book a demo to discover how Legit can strengthen your AI security strategy.