AI security is the specialized branch of cybersecurity that protects artificial intelligence and machine learning systems from theft, manipulation, unauthorized access, and data breaches. It covers two related but distinct areas: the security of AI protecting models, training data, and algorithms from attack and AI for security, which is using AI tools to automate and improve traditional threat detection.
This article focuses on the first meaning, since it’s the one most organizations mean when they ask how to secure the AI systems they’re building or deploying. As AI and LLM applications become more widely adopted, organizations also need dedicated security measures to protect these systems from emerging threats. AI and LLM security platforms can help provide this layer of protection across AI applications and deployments.
Core Focus Areas of AI Security
Securing models and data.
Defending training data, model logic, and inference endpoints against tampering, theft, or unauthorized access.
Controlling inputs and outputs.
Implementing guardrails so a model handles user interactions safely, without leaking sensitive information or producing harmful content.
Governance and lifecycle controls.
Establishing data lineage, accountability, and continuous monitoring across the multi-cloud and on-premise environments where AI systems actually run.
Common AI Threats
Data poisoning.
Tampering with training datasets to degrade model performance or inject hidden biases.
Prompt injection.
Manipulating inputs in generative AI tools to bypass safety filters and generate restricted or harmful outputs.
Adversarial attacks.
Using carefully crafted inputs designed to trick a machine learning model into making an incorrect decision.
Jailbreak attacks.
A specific, prompt-based technique for getting an LLM to bypass its own safety training common enough, and distinct enough from prompt injection, that it’s worth understanding on its own.
Shadow AI.
Unauthorized or unsanctioned AI applications deployed by employees, which can leak sensitive corporate data outside any security team’s visibility.
AI Security Best Practices
- Enforce strict role-based access controls and identity governance for AI services, so only authorized systems and people can query or modify a model.
- Encrypt sensitive data used in both training and runtime inference pipelines.
- Regularly audit and test models for operational drift and vulnerability to exploitation, not just at launch but continuously.
- Reference established frameworks, such as those from the Cloud Security Alliance, to maintain integrity, confidentiality, and availability across the AI stack.
Why AI Security Is a Different Problem Than Traditional AppSec
Most application security assumes the logic being protected is written by a developer and doesn’t change unless someone deploys new code. AI systems break that assumption. A model’s “logic” is learned from data, which means an attacker doesn’t need to find a bug in the code; they can shape the model’s behavior by shaping what it learns from, or by crafting an input clever enough to exploit how the model generalizes.That’s why data poisoning and adversarial inputs don’t have a direct equivalent in classic web or API security: the vulnerability isn’t a flaw in a function, it’s a property of the trained model itself, and it can’t always be patched the way a code vulnerability can.
This also means AI security overlaps heavily with API security in practice. Most production AI systems are exposed to the rest of an organization or to the internet through an API. Protecting the model itself matters, but so does protecting the inference endpoint from the same categories of abuse that threaten any other API: excessive requests, credential misuse, and unauthorized access to data the model was trained on or has access to at runtime.
This is where an AI security layer can sit directly in front of the application, inspecting requests before they reach the model. For LLM applications exposed through chat endpoints, RAG services, or AI agents, this approach allows security controls to cover both the AI-specific traffic and the underlying web and API surface in the same enforcement path.
Bringing AI Security Into the Broader Security Strategy
AI security matters precisely because it doesn’t fit neatly inside older security categories. A poisoned dataset or a well-crafted jailbreak prompt doesn’t look like a traditional exploit, and it often can’t be caught by the same tools that catch a SQL injection or a misconfigured server.
That’s not a reason to treat AI security as a separate, siloed discipline, though the practical reality is that most AI risk shows up at the same points organizations already monitor: the API in front of the model, the data feeding it, and the access controls around who can query or retrain it.
Getting AI security right means extending the discipline you already have access control, encryption, monitoring, API protection to cover a system whose “logic” is learned rather than written, while adding the AI-specific checks (data provenance, output filtering, adversarial testing) that traditional AppSec was never built to catch.
Frequently Asked Questions (FAQ)
1. What is security AI?
“Security AI” and “AI security” are often used to mean two different things, which is a common source of confusion. AI security (the subject of this article) means protecting AI systems themselves. Security AI, or “AI for security,” means using AI and machine learning techniques to strengthen traditional cybersecurity, for example, using anomaly detection models to catch intrusions faster than rule-based systems could.
2. How can AI be used in security?
AI is widely used to analyze large volumes of traffic or log data, detect malicious behavior patterns that would be too subtle or too voluminous for manual review, and flag anomalies in real time. This is the “AI for security” side of the field, a separate but related discipline from securing the AI models themselves.
3. What are the main types of AI security threats?
The most commonly discussed threats are data poisoning (corrupting training data), prompt injection and jailbreaking (manipulating a model’s inputs to bypass its safety behavior), adversarial attacks (inputs crafted to fool a model’s outputs), and shadow AI (unsanctioned AI tools used without security oversight).
4. Why is AI security important right now?
Because AI systems are moving from experimental to production-critical faster than most organizations’ security processes have adapted to cover them. A model making real decisions approving a loan, flagging fraud, powering a customer-facing chatbot is a high-value target, and the attack surface (training data, prompts, APIs, third-party models) is broader and less mature than in traditional software.
5. Is AI security part of API security?
They overlap significantly but aren’t identical. Most AI systems are accessed through APIs, so API security practices rate limiting, authentication, schema validation apply directly to protecting an AI system’s inference endpoint. AI security additionally covers concerns with no API equivalent, like training data integrity and model behavior itself.
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