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2026 AI Data Exposure Benchmark
Complete this 1-minute benchmark and we'll share the full results with you. See how your AI data leak concerns compare to other enterprise teams.
Which of the following AI data leaks concern you most? (Select all that apply)
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Which of the following AI data leaks concern you most? (Select all that apply)
Data-in-Use: AI inference runs in cleartext, making data visible to ops teams during processing.
Operational: Monitoring tools log every AI prompt (and context) by default.
Agent Identity: Neither agents nor the tools they use are verified before sensitive data is exchanged.
Agent Output: Agents send data to external destinations autonomously, without explicit per-destination authorization.
Access Control: RBAC policies enforced in source systems don't extend to the vector database layer.
AI Derivative: Deleted customer records persist in model embeddings, training snapshots, and cached weights.
Policy Gap: Data governance policies are checked at deploy time but not enforced during runtime execution.
Proof Gap: Evidence of what data was processed and which policies held during inference is unavailable for audits or compliance review.
Other
If you could address only one of the AI data leaks you selected, which would be your top priority?
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For which of these AI data leaks do you currently have a partial or full solution or workaround? (Select all that apply)
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For which of these AI data leaks do you currently have a partial or full solution or workaround? (Select all that apply)
Data-in-Use: AI inference runs in cleartext, making data visible to ops teams during processing.
Operational: Monitoring tools log every AI prompt (and context) by default.
Agent Identity: Neither agents nor the tools they use are verified before sensitive data is exchanged.
Agent Output: Agents send data to external destinations autonomously, without explicit per-destination authorization.
Access Control: RBAC policies enforced in source systems don't extend to the vector database layer.
AI Derivative: Deleted customer records persist in model embeddings, training snapshots, and cached weights.
Policy Gap: Data governance policies are checked at deploy time but not enforced during runtime execution.
Proof Gap: Evidence of what data was processed and which policies held during inference is unavailable for audits or compliance review.
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