Looking ahead, the author predicts increasing pressure on organizations to attest to the provenance and integrity of their data and AI lifecycle due to evolving customer and regulatory demands. The article suggests that organizations need to work on detecting AI threats and providing end-to-end visibility, protection, and compliance throughout the lifecycle to meet these demands.
Key takeaways:
- The rapid adoption of AI has revealed a critical blind spot for application security teams: the data and AI lifecycle, introducing significant, often overlooked, security and compliance risks.
- The data and AI lifecycle consists of distinct phases, from data collection and curation to model training, deployment and monitoring, all of which present new risks such as data pipeline misconfigurations, usage of sensitive data for training models against policy, exposed secrets in unscanned environments, and AI vulnerabilities.
- Addressing these risks requires a holistic approach that treats the entire lifecycle—not just the model—as the attack surface, ensuring security is built into every stage and environment.
- As organizations feel the pressure to drive innovation in competitive markets, AI will remain a powerful tool—but not without oversight and governance. Organizations must work to detect AI threats while providing end-to-end visibility, protection and compliance throughout the lifecycle.