Data Security and Insider Risk for AI Adoption
A new IDC Spotlight by analyst Jennifer Glenn, sponsored by Cyberhaven. See why data volume and sprawl put AI initiatives and insider risk programs at risk.
Why data volume and sprawl hide security risk
How DSPM and DLP work together for visibility
What changes when AI agents access your data
How Cyberhaven unifies discovery, DSPM, and IRM
Trusted by security teams at
By the numbers
Four Numbers Behind the Data Security and Insider Risk Gap
32%
Sensitive data stays unmapped
of respondents have over 75% of sensitive data mapped and monitored, despite nearly half of it being sensitive (IDC).
63%
External risk tops the list
of security teams report external data security issues, like suspected account compromise, to leaders (IDC).
58%
Confidential data leaks visibly
of teams flag confidential data vulnerability, such as source code exposed on GitHub, as a top concern (IDC).
55%
Insider risk reaches leadership
of security teams report internal malicious issues, like a laid-off employee downloading confidential data (IDC).
"Sensitive data is challenging to identify and classify, putting the business at risk from insider threats and AI exposure."