Data Security in the Age of AI: Proactive Strategies to Protect Your Most Valuable Assets

Why It Matters

Because AI magnifies existing data‑security gaps, integrating DSPM, DLP and UEBA is essential for protecting proprietary information, meeting stricter compliance regimes, and sustaining business‑critical AI deployments.

Key Takeaways

• Data sprawl expands attack surface across hybrid environments
• DSPM, DLP, UEBA form integrated security triangle for protection
• Insider threats now top AI‑related security concerns for enterprises
• Automated classification and remediation curb AI‑driven data leaks
• Unified platforms reduce silos and streamline compliance across teams

Summary

The webcast, led by Peter Sleven, senior information‑security manager at Bank of Ireland, examined how enterprises can safeguard data as AI adoption accelerates. Sleven framed data security as a prerequisite for successful AI projects and outlined a roadmap that spans challenges, trends, technology pillars and a NIS‑CSF‑aligned blueprint.

He identified five core challenges: data sprawl across hybrid clouds, limited visibility into sensitive assets, pressure to deploy AI without foundational controls, tightening regulatory scrutiny such as DORA, and entrenched point‑solution silos. Current trends countering these issues include a surge in insider threats, rapid uptake of data‑security‑posture‑management (DSPM) tools—used or evaluated by 94 % of firms—and a market shift toward consolidated platforms that blend CSPM, DLP and UEBA capabilities.

Sleven cited Zcaler’s 2025 “Data at Risk” report, which logged more than 4.2 million data‑loss incidents tied to generative‑AI tools. He illustrated a realistic breach: a finance analyst attempts to bulk‑export customer records to a personal OneDrive, triggering DSPM discovery, DLP blocking and UEBA‑generated risk scores. A parallel DLP example showed an email containing a password database being automatically blocked after content inspection and policy evaluation.

The takeaway is clear: a layered “security triangle” of DSPM, DLP and UEBA provides the visibility, control and behavioral analytics needed to lock down AI‑driven workflows. Organizations that adopt unified, cross‑functional platforms can reduce integration overhead, satisfy regulators and turn data‑security investments into a competitive advantage for AI initiatives.

Original Description

As organizations accelerate cloud adoption and embrace AI-driven innovation, maintaining visibility and control over sensitive data has become increasingly complex. Data now flows freely across on-premises systems, cloud platforms, and SaaS applications - often outpacing the reach of traditional security controls.

This dynamic environment increases the risk of data exposure, particularly as insider threats grow and cybercriminals target unprotected data. At the same time, organizations are under mounting regulatory scrutiny, making it more important than ever to safeguard sensitive information.

To meet these challenges, the next generation of solutions is maturing, offering more effective ways to manage data across its entire lifecycle - from discovery and classification to protection and monitoring.

In this webcast, we’ll explore:

- The evolving data security landscape and emerging threats

Industry trends driving the need for modern data protection

- A phased approach to implementing an effective data security program

- Real-world case studies with actionable insights

- Practical takeaways to support secure AI adoption and ongoing compliance

Visit https://go.sans.org/VmO1Zr to access the original presentation slides and unedited recording free of charge.

Learn more about Peter Slevin here, https://www.sans.org/profiles/peter-slevin

Browse courses, upcoming events, and all kinds of free tools and resources offered by the SANS Cybersecurity Leadership curriculum, https://www.sans.org/cybersecurity-leadership