Balancing Privacy and Visibility - Insider Threat Meets DLPNext's Chris Denbigh-White on Challenges of Data Protection in the Enterprise
Legacy DLP is broken due to excess complexity, extended time to value and misalignment with security and business goals, said Chris Denbigh-White, chief security officer at Next. He said that insider risk processes also are flawed and have historically been either highly intrusive or only initiated when something has already gone very wrong. It is crucial to strike the right balance between data protection and privacy, according to Denbigh-White.
By leveraging machine-learning algorithms directly on endpoint devices, organizations can analyze data locally and extract insights without compromising privacy, he said. This approach reduces the need for transmitting sensitive data to external servers, thereby preserving privacy while gaining valuable insights, he added.
In this video interview with Information Security Media Group at Infosecurity Europe, Denbigh-White discussed:
- Achieving the right balance between data protection and privacy;
- The advantages of leveraging machine-learning algorithms directly on endpoint devices;
- How to use pseudo-anonymization and data minimization to ensure privacy.
Denbigh-White has over 14 years of experience in the cybersecurity space including serving in the office of the CISO at Deutsche Bank as well as providing cyber intelligence for the Metropolitan Police.