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2026 · CURVE(TM) ReportSecure AI

Data Security for AI CURVE(TM) Report

As AI consumes enterprise data, the attack surface on the data layer explodes.

AI models are only as secure as the data pipelines that feed them. As enterprises deploy AI at scale, the attack surface on the data layer - training datasets, inference inputs, fine-tuning pipelines, and vector databases - has expanded faster than data security controls can keep pace. This report bridges AI Security and AI Governance, mapping the vendors protecting the data infrastructure that makes AI possible.

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Key Findings

  • 1Vector database security is the most underprotected data layer in enterprise AI deployments
  • 2DSPM vendors with AI-native capabilities are growing 3x faster than traditional DLP incumbents
  • 3RAG pipeline data exposure is the most common source of AI-related data breaches in 2025
  • 4Only two vendors have native controls across both training data and inference output security
  • 5AI output monitoring is emerging as a standalone category - it will consolidate into DSPM within 18 months

Inside the Report

What's covered

01

The AI Data Attack Surface

A map of where enterprise data is exposed in the AI lifecycle: training ingestion, fine-tuning pipelines, RAG datastores, inference logs, and output caches.

02

Data Pipeline Exposure

How sensitive data flows through AI pipelines, where it accumulates, and the specific risks at each stage - with incident composites.

03

AI Training Data Risk

The risks of training on enterprise data: intellectual property leakage, PII exposure, poisoning via data injection, and regulatory liability.

04

Vendor Landscape on the CURVE(TM)

Data security vendors evaluated for AI-native capabilities - from DSPM and DLP to vector database security and AI output monitoring.

05

Governance Integration

How data security platforms integrate with AI governance programs - the vendors that bridge both, and why that integration is the next competitive frontier.

Who Should Read This

  • Chief Data Officers building AI data governance programs
  • CISOs responsible for data security in AI deployments
  • Data engineers managing AI training and inference pipelines
  • Compliance officers navigating GDPR and CCPA in AI contexts
  • Security architects selecting DSPM and DLP platforms for AI-era enterprises

Report Details

Published2026 Edition
PillarSecure AI
FormatPDF - Free Download
IndependenceNo pay-for-placement

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Independent by design. Not pay-for-placement.

No vendor pays to appear in a CURVE(TM) Report, influence a tier, or shape a finding. Reprint rights are the only commercial relationship - purchased after publication, never before. The editorial firewall is the product.

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