Artificial intelligence security learning resources
AI security has emerged as a critical focus of organizational cybersecurity. As AI technologies continue to evolve, organizations must prioritize robust risk management and the protection of sensitive data throughout their lifecycle. Vulnerabilities must be identified and mitigated before applications can be compromised. Learn how you can manage security risks and enhance AI application resilience against cyber threats with F5's wealth of resources below.
What is AI security?
AI security is the practice of protecting AI models, applications, agents, and APIs from malicious attacks, data leaks, and other unauthorized behavior. Taking a multi-layered approach to AI security often involves filtering AI system inputs and outputs, implementing guardrails, and working to find and fix vulnerabilities before attacks occur.
While working to secure AI systems, organizations can also incorporate AI capabilities into cybersecurity. For example, cybersecurity tools that use machine learning (ML), a form of AI, can enhance threat detection by identifying unusual behavior and automating responses.
AI API security
Safeguarding APIs is the first step to securing applications that interact with AI models. Learn how to protect applications against unauthorized access, data breaches, and misuse.
Generative AI cybersecurity risks
Understand the unique vulnerabilities and risks associated with generative AI technologies and learn how to mitigate these emerging threats.
ML security
Dive into how to secure machine learning models and the associated data, preventing model theft, corruption, and other LLM threats.
AI inference
Discover how to protect and optimize the AI inference process, ensuring resilient and secure outputs. Secure data and prevent manipulation or misuse of AI models.
AI attack and threat mitigation
Learn about common AI attack vectors and how to implement effective mitigation strategies to protect sensitive data and safeguard applications.
AI adoption and innovation
Explore how to securely integrate AI into your business, balancing innovation with necessary security measures to ensure responsible AI implementation.
Frequently asked questions
What is the difference between traditional web application security and generative AI security?
Traditional web security protects deterministic application code, fixed API endpoints, and structured databases. Generative AI security protects probabilistic models that interpret natural language as both input and execution code. Generative AI security can be difficult because user input and malicious instructions can be blended together, unlike with typical web application threats.
How do you prevent prompt injection attacks in enterprise LLMs?
Prompt injection occurs when attackers insert malicious instructions into user input or third-party sources. Enterprises should take a defense-in-depth approach to prevention that includes implementing guardrails, red teaming (to find and fix vulnerabilities early), and filtering both input and output.
What are the main security risks associated with autonomous or agentic AI?
According to the Open Web Application Security Project (OWASP), the top three security risks for AI agents are goal hijacking, tool misuse, and identity abuse. Goal hijacking is when attackers manipulate agents to refocus on unauthorized objectives. Tool misuse occurs when attackers make an agent select the wrong tools or use tools in an unintended way. With identity abuse, attackers exploit valid permissions to execute malicious actions.
What framework should organizations use to manage AI security vulnerabilities?
The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) can help organizations design, deploy, and govern AI systems responsibly and securely. This is a structured, voluntary blueprint that focuses on governing AI, mapping vulnerabilities, measuring safety, and managing AI risks.
What is shadow AI, and how can organizations control it?
Shadow AI is the use of unsanctioned AI tools by employees without approval or oversight from IT or security. To control shadow AI, organizations need a solution that can provide visibility into even encrypted traffic, route traffic into inspection tools, and apply granular policies for allowing or blocking traffic to certain tools or sites.