The rapid adoption of artificial intelligence (AI) across enterprises is transforming decision-making and efficiency. But with these benefits come new data security risks—ranging from inadvertent exposure of sensitive PII to gaps in existing data loss prevention (DLP) programs. Traditional controls, built for data at rest and in transit, often fail to keep pace with the dynamic nature of AI deployments and the growing volume of business data being introduced into large language models (LLMs).
This webcast will explore where conventional DLP programs fall short in the age of AI, including lack of context, overwhelming alert fatigue, and ineffective measurement. Attendees will learn how risk-based guardrails, automation, and integrated reporting can help close these gaps, enabling organizations to confidently deploy AI while safeguarding sensitive data.
What You Will Learn
- AI’s Impact on Data Security: How new AI-driven workflows expose gaps in traditional DLP and compliance programs
- Common Pitfalls: Why disparate toolsets, poor labeling practices, and weak measurement strategies leave enterprises vulnerable
- Risk-Based Guardrails: Practical ways to align AI adoption with enterprise security requirements through risk management frameworks
- AI Deployment: What to consider when incorporating AI into a DLP program
- Scaling Securely: Strategies to streamline data classification and risk monitoring across SaaS environments such as M365, file sharing, and email
