Original Post: Infosec Europe session: 4 tips for safer AI adoption
The article discusses the rapid adoption of AI technologies in businesses, noting that it is advancing at twice the speed of early internet adoption. Highlighted are the security challenges that come with integrating AI, such as insecure code suggestions and vulnerabilities, and the need for careful implementation to mitigate risks.
Key suggestions for CISOs include:
- Classify AI Usage: Determine the criticality of AI implementation across different business areas to manage risks effectively.
- Don’t Rely Solely on LLMs: Use additional resources and guardrails to test and secure AI-generated code instead of relying entirely on large language models (LLMs).
- Protect Training Models: Safeguard AI training models from attacks like prompt injection by involving security teams in the training process and ensuring the input data is validated.
The article stresses the importance of knowing the AI tools within the tech stack, as many third-party applications may include AI features without full disclosure. Enterprises should inventory these applications, prioritizing business-critical areas first.
Snyk offers solutions to integrate seamlessly with development workflows, providing SAST scans, vulnerability fixes, pull request checks, and context-driven insights to aid in secure AI development. The article concludes by encouraging the adoption of tailored security practices to keep pace with AI advancements securely.
For more detailed guidance, readers are referred to Snyk’s cheat sheet for secure AI development.
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