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

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

by Peter Slevin

Applied Sciences & Engineering Computing & Cybersecurity URComputer securityURDPrivacy and data protection
🎙 Peter Slevin 👥 70K 📅 March 6, 2026 ⏱ 42 min 👁 782 📄 expert opinion 🧭 2026-08-15
Available in: English (current) Français

Keywords

data securityAIDSPMDLPUEBA

Summary

Peter Slevin, a senior information security manager at Bank of Ireland, presents a comprehensive overview of data security challenges and strategies in the age of AI. He identifies key challenges including data sprawl, lack of visibility, AI readiness, regulatory scrutiny, and siloed tools. He then discusses industry trends such as the rise of insider threats, the emergence of DSPM (Data Security Posture Management), technology convergence, and cross-functional tooling. The core of the talk focuses on three complementary technologies: DSPM for data discovery and classification, DLP (Data Loss Prevention) for controlling data movement, and UEBA (User and Entity Behavior Analytics) for detecting anomalous behavior. He illustrates how these work together through practical scenarios. He then addresses specific AI-related risks: sensitive data exfiltration via prompts, shadow AI, unclassified overexposed data, overpermissioned users and agents, and lack of audit/detection capabilities. For each, he provides real-world examples and mitigations. He concludes with a blueprint aligned with the NIST Cybersecurity Framework, case studies, and actionable takeaways for secure AI adoption.

165 words

Critical Evaluation

Value of the Information & Strength of the Argument

The presentation offers valuable, actionable insights for organizations looking to enhance their data security posture, especially in the context of AI adoption. The speaker’s professional background lends credibility, and he supports his points with references to industry reports and real-world incidents (e.g., Samsung’s ChatGPT ban). The argumentation is solid, logically structured from challenges to trends to technologies to AI-specific risks and mitigations. He emphasizes a layered defense approach, integrating DSPM, DLP, and UEBA, which is a practical and effective strategy. The use of concrete scenarios (e.g., finance employee exporting data) makes the concepts tangible. However, some claims lack specific citations within the talk, and the reliance on vendor reports could introduce bias.

Scientific Rigor, Source Quality, Title Accuracy

The speaker demonstrates a good understanding of the subject and references several industry reports (e.g., Zscaler, Futurum, Gartner) and frameworks (NIST CSF). However, he does not provide detailed citations for all statistics, and some sources are mentioned without specific publication details. The title accurately reflects the content, which is focused on data security strategies in the AI era. The presentation is well-structured and aligns with the stated objectives. The inclusion of case studies and practical examples enhances its credibility, though the lack of peer-reviewed sources limits its scientific rigor.

216 words

Title / Content Match

The title accurately reflects the content, which focuses on data security challenges and strategies in the context of AI adoption.

Quality & Reliability

8/10

The presentation is given by a senior information security manager at Bank of Ireland, providing practical insights and referencing industry reports (e.g., Zscaler, Futurum, Gartner). While not peer-reviewed, the content is grounded in professional experience and recognized frameworks (NIST CSF).

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Concurring Sources

  • Zscaler 2025 Data at Risk Report — Cited for statistics on GenAI data loss violations.
  • Futurum Research on DSPM adoption — Cited for the 94% DSPM adoption statistic.
  • Gartner predictions on technology convergence — Cited for the trend of consolidation in data security tools.

Contribution & Novelties

The presentation provides a practical, integrated approach to data security in the AI era, emphasizing the combination of DSPM, DLP, and UEBA as a layered defense. It offers actionable insights for organizations, particularly in regulated industries. The speaker’s real-world experience adds value, and the focus on AI-specific risks (e.g., shadow AI, overpermissioned agents) is timely.

Pour aller plus loin :

  • Data Security Posture Management (DSPM) — Gartner’s definition and market guide.
  • NIST Cybersecurity Framework — The framework referenced for the blueprint.
  • User and Entity Behavior Analytics (UEBA) — Gartner’s market guide for UEBA.
  • Data Loss Prevention (DLP) — Gartner’s market guide for DLP.
  • Zscaler Data at Risk Report — Report cited for AI data loss statistics.

116 words

Radar Profile

The radar profile shows high scores in quantity and quality of information, with a slightly lower but still solid score in technical depth. This indicates a comprehensive and reliable presentation, though it may not delve into the most advanced technical details, making it accessible to a broad professional audience.

Reliability 8/10