Maturing AI Security
Throughout this book, we’ve examined the threats that are posed by adversarial AI and considered practical mitigations. In the previous two chapters, we incorporated these threats and mitigations into a more holistic AI application security approach by using a secure-by-design AI methodology and applying MLSecOps to embed AI security throughout the life cycle. These are essential steps to safeguard AI solutions, but their effectiveness will depend on how well they integrate with the broader enterprise AI security. This aligns with the organization’s goals, security standards, and compliance requirements. This alignment ensures that AI security is not a siloed endeavor but a well-integrated part of the organization’s overall risk management and governance frameworks. In this final chapter, we will understand the essential elements of enterprise AI security that will allow us to align and mature AI security. This will follow the five functions...
