The ultimate guide to AI security: key AI security risks, vulnerabilities and strategies for protection. 61% of companies use AI, but few secure it. This whitepaper covers the key AI risks being overlooked from LLMs to RAG.
Inside the Securing GenAI whitepaper:
- GenAI attack surface – Covers where we are in handling AI security as an industry, where we have major gaps, and the dilemmas faced by security professionals as new AI features are introduced via existing vendors, new vendors, internal dev teams, and individuals.
- The AI ecosystem – A quick introduction to Retrieval Augmented Generation (RAG), semantic search powered by AI and vectors, and when, why, and where these technologies appear in modern GenAI systems.
- LLM-specific risks – We walk through the OWASP Top 10, giving brief descriptions of each item and addressing whether or not it concerns those who aren’t making large language models since roughly half of the issues covered by OWASP are fairly specific to the big LLM producers like OpenAI and Google.
- RAG risks – Today, OWASP and others have largely failed to cover risks associated with RAG and other parts of the AI ecosystem. This chapter comprehensively covers everything from leaky pipelines and logs to direct and indirect prompt injections, to vector embedding inversions, and to using AI as “agents” in RAG workflows.
- AI security solutions – This chapter goes into detail on each type of AI security measure on the market today. It calls out the subvariants, what to look out for, how they’re confused, and example vendors for each type of solution.
- Vendor questionnaire – Fifteen questions to help you evaluate your software vendors as they add AI features. This questionnaire will help you assess their level of maturity in securing the data that flows through their new AI systems to help you make decisions about risk and reward and to see where data may be at risk across your infrastructure.
- Recommended AI security approach – This section gives opinions on the policies that security teams should set for any use of AI within their organization including, and mainly focusing on the types of mitigations and security tools that should be in place before experimental AI features are working with live data in real environments.
Download: Securing GenAI whitepaper