Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Earlier this year, security researchers found more than 1 million records, including user data and API keys, in an exposed DeepSeek database. This massive exposure event tells us that data exfiltration risk and AI proliferation are forever linked together: as AI tools grow in popularity and complexity, exfiltration risk rises in kind.

Security leaders in the life sciences and health technology fields know how important it is to safeguard sensitive data like protected health information (PHI), personally identifiable information (PII), and confidential research data. They also know what’s at stake with a security breach or data exfiltration event. But what’s not always clear is how to find the right solution to keep all that data safe.

Legacy DLP solutions, as well as CASB and app-native DLP solutions, face significant challenges in providing comprehensive coverage across modern SaaS, AI apps, and endpoints. Lack of visibility, clumsy deployments, and expensive implementations are common drawbacks of using these tools — and they leave big gaps in data loss prevention. Even today, we’re still seeing the same problems that have persisted for decades in today’s DLP solutions.

Insider risk is a tricky challenge for security teams: how can you tell the good actors from the bad, or intentional actions from mistakes? Anyone with approved access to endpoints and SaaS systems could expose data to exfiltration risk if those systems are focused solely on preventing outsiders from getting in.

‍ In the modern workplace, data exposure can happen in mere seconds. Legacy data loss prevention tools have often been considered bulky, manual, and time-intensive—especially when it comes to alert resolution and user training.