What is AI Security?

Overview of AI Security

AI security is the practice of protecting AI and machine learning systems, including models, training data, and inference infrastructure, from theft, tampering, and misuse across the software supply chain that builds and ships them.

AI security vs. security of AI

The term AI security can be used interchangeably to describe two different concepts. AI can be used as a powerful cybersecurity tool, but AI security also refers to the security of AI models and systems. This page covers the security of AI and includes exploits like prompt injection, data poisoning, and shadow AI.

AI safety vs. AI security

AI safety and AI security are also two unique concepts. AI security is focused on protecting AI systems against cyberattacks and other threats, while AI safety focuses on ensuring AI models don’t produce biased results or cause unintentional harm to users.

AI security vs. cybersecurity

The key difference between AI security and traditional cybersecurity is that AI is more complex and dynamic than traditional software. Traditional cybersecurity is relatively simple because software is static and exploits are easier to identify and patch. AI faces a wider range of threats because it’s more dynamic, which means it requires continuous monitoring and protection.

Why Does AI Security Matter for the Software Supply Chain?

As AI becomes an increasingly crucial resource for businesses, AI security posture management is a main focus for businesses. AI security has an impact on everything from compliance and downtime to customer trust.

In terms of business impact, AI can result in intellectual property loss when you don’t have control over the outputs it generates. Poor AI cybersecurity can also result in downtime that impacts your bottom line, and it can impact the ability to maintain compliance with regulatory frameworks like the EU AI Act and NIST AI RMF.

AI security matters for DevSecOps and MLOps teams because insecure CI/CD, model registries, and container images act as attack vectors. These attack vectors can be used to execute code, manipulate outputs, and steal proprietary data, which can have a substantial impact on your bottom line.

Strong AI security isn’t just about preventing attacks and maintaining compliance; it’s also about building trust with your customers. Model tampering and bias exploitation make it difficult for users to trust AI models, which can also hurt your bottom line.

An agentic supply chain helps protect against some of the potential risks surrounding AI. Essentially, it’s a supply chain where AI agents manage the software supply chain and identify potential threats and unusual behavior.

The AI Attack Surface: Where Risk Enters

Understanding how attacks occur can help you gain a better understanding of AI security tools. There are several potential attack surfaces with AI, including training data, model weights, inference endpoints, agent/orchestration layers, and third-party model or package dependencies.

Here’s a breakdown of the threats AI models face:

• Data poisoning: The process of corrupting training data to encourage bias or break model behavior. Users intentionally input data that causes an AI model to learn incorrect behavior, which impacts the outputs the model generates when a user inputs a prompt.
• Model theft: Attackers send queries to your AI model and analyze the outputs to train a shadow model. The goal is to replicate the functionality of your proprietary AI model so it can be used without authorization.
• Model inversion: Attackers use targeted inputs to identify confidential records that were used to train an AI model, such as financial data. This can result in exposure of sensitive information and a substantial loss of customer trust.
• Adversarial attacks and prompt injection: Some AI attacks are as simple as manipulating inputs to produce unintended model behavior. In some cases, AI models will read instructions within a prompt and execute them even if they shouldn’t.
• Supply-chain tampering and shadow AI: When teams pull malicious or unvetted models from public model hubs (such as Hugging Face) or bring compromised dependencies into the pipeline, there’s an AI security risk.

What are the Key Pillars of a Practical AI Security Strategy?

A strong AI security strategy can help you protect against the threats that can substantially impact your profit and cause you to lose customer trust. Combining multiple strategies and utilizing resources like MCP registries and an AIBOM can help you keep your AI model secure.

Data protection and provenance are a core aspect of NIST AI RMF. Tracking model lineage requires linking code repositories, training datasets, hyperparameter runs, and final weights across MLOps tools (e.g., Git to MLflow to production inference registries) to ensure full visibility.

Model lifecycle controls (enforced via policy-as-code rules) ensure access control, strict artifact versioning, automated binary weight scanning, and adversarial/robustness testing before deployment. This way, you can safely roll back your AI model if something goes wrong.

Runtime protections prevent attacks once an AI model is live. Security teams should monitor for anomalies, validate inputs, and use least-privilege inference to ensure nobody has more access to AI tools than necessary.

NIST AI RMF and OWASP LLM Top 10 are the two common standards for AI security. These frameworks guide you through building a strong AI security strategy.

AI Security Across the Software Supply Chain

AI security isn’t just a cloud or infrastructure issue; it can also be a software supply chain problem.

• ML Model registries and tracking tools (such as MLflow, Weights & Biases, or enterprise registries) act as a control point to ensure only curated, scanned models reach production inference.
• AI bill of materials (AIBOMs) are the AI-specific equivalent of an SBOM. They extend standard machine-readable formats (such as SPDX or CycloneDX) to capture model weights, training dataset hashes, base environments, and pipeline dependencies.
• Vulnerable or untrusted source models and packages are scanned directly for embedded malicious code (e.g., pickled payloads or arbitrary code execution in model binaries) and automatically blocked via policy-as-code gatekeeping before entering the CI/CD pipeline.
• MCP servers and agent skills repositories extend the software supply chain into agentic AI.

Explore the AI Security Learning Cluster

• Shadow AI: The unauthorized use of AI tools and models without oversight from IT or security teams.
• AI Governance: The policies and accountability structures for responsible AI development and use.
• AIBOM: A structured inventory of the models, datasets, and dependencies inside an AI system.
• MCP Registry:  Governance and security for Model Context Protocol (MCP) servers used by agentic AI.
• Agentic Supply Chain: Securing the tools, permissions, and dependencies autonomous AI agents rely on.
• Agent Skills Repository: Governance for the reusable skills and capabilities agents use.
• AI Gateway: Routing, cost, and access control across model providers.
• AI Security Posture Management (AI-SPM): Continuous visibility into AI asset risk.
• ML Model Registries: Centralized systems (e.g., MLflow) for tracking, versioning, managing metadata, and controlling the deployment lifecycle of trained ML models.

What are Best Practices for Strengthening AI Security?

Following best practices for strengthening AI security helps you protect against the many risks AI faces. Here’s a simple checklist IT decision-makers can use to strengthen AI security:

• Discover and inventory every new asset, including models in production, inference endpoints, and training pipelines.
• Assess and prioritize risk using AI-specific threat modeling rather than traditional threat models.
• Implement governance and access management, plus AI-specific protection like adversarial testing and drift monitoring.
• Monitor continuously and treat model anomalies as security incidents that trigger response workflows.
• Build cross-functional ownership between security, MLOps, and platform teams.

How JFrog Can Help Secure AI Systems

The JFrog Platform secures AI at the supply chain layer, securing artifacts, models, and packages as they move through the AI/ML supply chain from development to production. Instead of treating AI security as monitoring, JFrog governs the entire AI lifecycle.

• JFrog AI Catalog: provides discovery, governance, and security across the entire AI ecosystem.
• JFrog ML: the all-in-one solution to build, deploy, manage, and monitor all your AI workflows, from GenAI and LLMs to classic ML.
• JFrog Curation: automatically vets AI models and software packages.
• JFrog Xray: scans AI artifacts and models for potential risks.
• JFrog Advanced Security: extends supply chain protection to secrets, misconfigurations, and runtime risk across AI-enabled applications.

Schedule a demo or start a free trial of the JFrog Platform to see how end-to-end provenance and real-time governance can secure your AI system today.