Data Privacy Day is becoming less about awareness and more about readiness. In IT Brief’s latest coverage, security and infrastructure leaders warn that AI and cloud adoption are moving faster than many organizations’ ability to govern personal data—at the same time boards and regulators are asking tougher questions about controls, visibility, and recoverability. Commentary from Protegrity, StorMagic, and Commvault frames the challenge clearly: privacy risk now sits at the intersection of AI pipelines, infrastructure choices, and the ability to prove you can contain impact and restore cleanly when incidents happen.
What’s in the piece
- AI governance: Protegrity’s Milan Chutake warns privacy must be addressed before data reaches a model, and calls out the lack of centralized policy, consistent controls, and audit-ready visibility as AI scales across business units.
- Infrastructure choices: StorMagic’s Bruce Kornfeld argues privacy-by-discipline starts with infrastructure decisions—especially where strong governance, ownership, and predictable performance matter.
- Test assumptions (privacy + resilience): Commvault’s Gareth Russell emphasizes that expectations are shifting from policy intent to demonstrable proof: can organizations contain impact, restore cleanly, and sustain trust through disruption—especially as identity compromise remains a common path to sensitive cloud data.
Why it matters
Data Privacy Day is increasingly a board-level checkpoint: not just “do we have policies,” but “can we prove control under pressure?” As AI adoption accelerates, privacy programs are being judged on enforceable governance (policy + logging + visibility) and on operational outcomes like tested recovery, clean restoration, and limiting blast radius when identity controls fail.
Key shifts highlighted
- From AI acceleration → AI accountability: scaling AI without centralized governance increases the odds sensitive data enters workflows without consistent controls.
- From “policy exists” → “proof exists”: audit-ready logging, visibility, and evidence are becoming as important as written policy.
- From breach prevention → recoverability as privacy: resilience and clean recovery are framed as trust capabilities when personal data is involved.
- From cloud-first assumptions → infrastructure as a privacy control: where data lives (on-prem/edge/cloud) shapes visibility, ownership, and enforcement.
- Identity as the fault line: compromised identities remain one of the fastest routes to sensitive data in cloud environments.
Protegrity perspective
Chutake’s core message is simple: privacy controls have to operate before sensitive data ever touches an AI model. Once regulated data enters an AI pipeline or external LLM, reversing exposure is extremely difficult. The path forward is centralized, enforceable policy, continuous logging, and end-to-end visibility into how data is discovered, shared, accessed, and used—paired with human oversight so teams don’t blindly trust AI with sensitive information.
How Protegrity helps
- Protect sensitive data before AI exposure: apply tokenization, encryption, masking, or anonymization so data remains usable without being fully revealed.
- Enforce consistent policy across systems: keep controls aligned across cloud, on-prem, SaaS, and hybrid workflows as AI expands into more business units.
- Support audit-ready governance: strengthen visibility and defensibility with logging and evidence to prove how data is accessed and used.
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