7 Ways Kiteworks Boosts Your DSPM Investment to Stop Shadow AI Data Sharing

Data Security Posture Management (DSPM) gives security teams the data-centric visibility and policy framework they need to address shadow AI—the unsanctioned use of AI tools that move sensitive data outside governance.

However, DSPM alone cannot fully stop shadow AI data sharing risk. DSPM discovers, classifies, and maps sensitive information across clouds, endpoints, and integrations, but only when integrated with a tool that enforces policies at egress, applies access controls, and tracks data access and movement can organizations effectively prevent AI ingestion and exfiltration.

Kiteworks complements your DSPM by combining unified visibility of data in motion, policy enforcement at egress, and incident response. Together, Kiteworks and DSPM help organizations catalog their data, track its flow, identify who accesses it, and understand how AI interacts with it—then apply encryption, zero-trust access, and audit trails to keep it compliant and contained. This is the practical path to data security posture management for AI at scale, focusing not just on point-by-point blocking but on continuous governance.

Powered by the Kiteworks AI Data Gateway, organizations can enforce privacy-by-design controls on every AI interaction—mediating prompts and responses, masking or tokenizing sensitive elements before they reach a model, routing traffic only to approved AI providers, and maintaining full chain-of-custody logging.

Executive Summary

Main idea: Combining DSPM’s discovery and classification with Kiteworks’ AI Data Gateway enforcement turns visibility into real-time prevention—mediating prompts and responses, controlling egress, and proving compliance to stop shadow AI data sharing.

Why you should care: Shadow AI is pervasive and risky; without governed AI egress controls, sensitive data, IP, and regulated records leak to models. Pairing DSPM with Kiteworks reduces ingestion/exfiltration, accelerates incident response, and aligns AI use with GDPR, HIPAA, and CCPA.

Key Takeaways

1. Visibility-first governance. Augment DSPM with data-in-motion inspection to inventory AI endpoints, extensions, and flows, with user-to-model attribution and risk context via the AI Data Gateway.

2. Policy enforcement at egress. Apply allow/deny, redact, mask, tokenize, or block pre-prompt and post-response; enforce zero-trust access and end-to-end encryption to keep sensitive data private-by-design.

3. Rapid incident response. Real-time alerts, automated playbooks, and forensic, tamper-proof logs shrink dwell time and speed containment across shadow AI exposures.

4. Compliance you can prove. Map data inventories to regulations, enforce minimization and retention, and maintain immutable audit trails to meet reporting and accountability obligations.

5. Seamless integration and education. Integrate with DLP, SIEM/SOAR, CASB, and identity; deliver just-in-time user prompts and role-based training to reduce risky behavior at the moment of action.

Kiteworks with DSPM Enhances Shadow AI Visibility

Shadow AI occurs when employees use unapproved AI tools, browser extensions, or integrations without IT’s knowledge, creating uncontrolled data flows and blind spots. As enterprise generative AI adoption surged from 74% to 96% between 2023 and 2024, shadow AI risks rose accordingly; 38% of employees admit to sharing sensitive data with AI tools without permission, according to IBM’s overview of shadow AI risks (see IBM’s perspective on shadow AI). Organizations address this head-on by augmenting DSPM with data-in-motion visibility and controls. Through the AI Data Gateway, AI-bound traffic can be proxied and inspected at the privacy layer, providing prompt-level visibility, user-to-model attribution, and policy context without exposing regulated data.

DSPM plus enforcement maps:

• Unsanctioned AI endpoints (web apps, plugins, mobile apps) that touch enterprise data

• Browser extensions and third-party connectors drawing data from email, file shares, and SaaS

• Data flows in motion, including uploads to AI services, API calls, and copy/paste events

• Users, devices, and identities involved in AI interactions across managed and unmanaged channels

Why visibility first? You can’t protect what you can’t see—accurate inventories of AI tools and data flows are prerequisites to effective policy enforcement and protection against AI data ingestion. A privacy-aware gateway baseline also ensures you know which prompts, responses, and models are in play before you set controls.

For foundational context on the DSPM approach that enables this visibility, see Palo Alto Networks’ overview of what DSPM covers (what is DSPM).

Kiteworks with DSPM Classifies and Protects Sensitive Data from AI Ingestion

Data classification is the process of identifying and labeling information based on sensitivity and regulatory requirements so policies can be enforced consistently. DSPM automates the discovery and classification of PII, PHI, source code, contracts, and intellectual property; an enforcement layer then applies those classifications to ensure only approved AI platforms—and only authorized users—can access or ingest this data. The AI Data Gateway operationalizes privacy controls inline, enforcing data minimization and model allow/deny policies on both prompts and outputs so sensitive information is never exposed to a model unless policy explicitly permits it.

Real-world incidents prove the stakes: employees pasting customer data or source code into public AI tools have triggered high-profile leaks, including the Samsung engineers’ code disclosure (see shadow AI examples and the Samsung case). Shadow AI breaches expose 65% more personally identifiable information and 40% more intellectual property than other incidents, underscoring the crucial need for precise controls (shadow AI stats report).

Operationalize protection at the moment of AI risk:

• Granular policies: allow, redact, mask, tokenize, or block data sent to AI based on labels and context—applied pre-prompt and post-response to preserve privacy

• End-to-end encryption: protect sensitive data at rest and in motion within the Private Data Network

• Zero-trust access: verify user, device, and risk signals before permitting AI interactions

• Tamper-proof logging: immutable records of AI-related access and transfers for forensics, including prompt and response telemetry

For organizations enabling governed AI, Microsoft’s guidance on DSPM considerations for AI aligns with this classification-to-control approach (Microsoft DSPM for AI considerations).

Kiteworks with DSPM Enables Rapid Incident Response for AI-Related Data Exposure

Incident response is the coordinated process to detect, contain, eradicate, and recover from a breach. When shadow AI exposes data, speed and precision are essential. Research shows 97% of AI-related breaches lacked proper access controls—misconfigurations, oversharing, and unmanaged endpoints amplify the impact (shadow AI stats report).

Integrated enforcement accelerates AI incident response with:

• Real-time alerts on sensitive data transfers to unapproved AI tools

• Automated playbooks to quarantine content, revoke shares, and rotate credentials

• Forensic-grade logs that trace who shared what, with which AI, from which device, and when

A typical response workflow:

1. Detect: Alert triggers at the AI Data Gateway on abnormal AI-bound data movement.

2. Triage: Classify incident severity using data labels, user risk, and AI tool reputation.

3. Contain: Auto-block further exfiltration at the gateway; isolate affected accounts and repositories.

4. Investigate: Correlate identity, device, and session details; review immutable audit trails, including prompt/response history.

5. Eradicate: Remove residual shares, revoke tokens, and disable risky integrations.

6. Recover: Restore clean data states; validate policy coverage and fix gaps.

7. Learn: Update rules, user prompts, and approved AI lists to prevent recurrence.

Kiteworks with DSPM Supports Regulatory Compliance for AI Data Usage

Compliance means adhering to frameworks such as GDPR, HIPAA, and CCPA that govern privacy, security, and reporting. Shadow AI creates regulatory blind spots—unsanctioned tools can trigger legal penalties and reputational damage when they process regulated data outside governance (why shadow AI heightens compliance risk).

Extend your DSPM compliance discipline to AI:

• Data inventory mapped to regulations (GDPR/HIPAA/CCPA)

• Continuous monitoring of AI-related access and transfers

• Encrypted transfer and storage, with key management controls

• End-to-end audit trails for investigations and reporting

• Policy-based data minimization and retention, enforced at the AI Data Gateway to uphold privacy-by-design and purpose limitation

For deeper insight into risk and governance patterns tied to AI, see Kiteworks’ analysis of the AI data security crisis in 2025 (Kiteworks: AI data security crisis 2025).

Kiteworks with DSPM Integrates Employee Education to Reduce Shadow AI Risks

Human behavior drives most shadow AI exposure. Forty-three percent of employees report sharing sensitive data with AI tools without employer knowledge, often with good intentions but poor outcomes (shadow AI stats report). Technology plus training is the remedy.

How to reduce risky behavior:

• Just-in-time prompts: Inline warnings when users attempt to paste or upload sensitive data to AI—delivered through the AI Data Gateway at the moment of action

• Policy reminders: Contextual guidance on approved AI tools and safe sharing options

• Targeted training: Role-based education informed by user risk patterns and incidents

Key behaviors that lead to shadow AI risk—and how education helps:

• Copy/pasting client or patient data into public AI chats → teach redaction/tokenization and approved AI channels

• Uploading code snippets for debugging → provide secure, sanctioned AI environments with logging

• Installing AI browser extensions → require extension approval and explain data collection risks

• Using personal AI accounts for work → enforce SSO and clarify acceptable use policies

Kiteworks with DSPM Seamlessly Integrates with Existing Security Frameworks

Unauthorized AI often operates outside traditional controls, making integration critical to expand coverage (why shadow AI escapes standard controls). Complement DLP, SIEM, and CASB—as well as your DSPM—to unify governance across sanctioned and unsanctioned AI endpoints. As a governed proxy between users and LLMs, an AI Data Gateway centralizes policy enforcement and privacy controls while feeding rich telemetry to existing security stacks.

What integration looks like:

• APIs and webhooks to share context with SIEM/SOAR for correlated detections and automated response

• CASB alignment to enforce app-based controls while Kiteworks covers data-in-motion and ungoverned channels

• DLP augmentation with true data posture awareness—labels, repositories, and AI-specific policies

• Identity integration (SSO/MFA) to enforce least-privilege, device posture checks, and conditional AI access

• Centralized dashboards to track AI interactions, policy hits, and compliance KPIs in one place

For additional perspective on securing data in motion—often the blind spot exploited by shadow AI—see Kiteworks’ DSPM for data in motion guidance (Kiteworks: DSPM for data in motion).

Kiteworks with DSPM Conducts Regular Audits to Identify Shadow AI Threats

Shadow AI is a moving target; 29% of employees pay for AI tools out of pocket, creating unmonitored channels that appear and evolve quickly (shadow AI stats report). Routine audits uncover new tools, integrations, and behaviors before they trigger breaches.

A typical AI-related DSPM audit cycle with DSPM and Kiteworks:

• Scope: Define business units, repositories, and AI endpoints to review

• Discover: Refresh AI tool inventory; scan for new extensions and integrations

• Analyze: Correlate data flows, labels, and anomalous access patterns

• Validate: Test policies against real AI interactions; simulate egress scenarios

• Remediate: Update allowlists/blocklists; refine just-in-time prompts and user training

• Report: Produce executive-ready metrics and evidence for auditors and regulators

Sample audit checklist:

• Are all AI tools and extensions inventoried and risk-rated?

• Is sensitive data consistently labeled across repositories?

• Are policies blocking unapproved AI ingestion effective and tested?

• Are incident response playbooks current and exercised?

• Do audit trails capture end-to-end AI interactions for at least the required retention period?

Operationalize DSPM for AI with the Kiteworks AI Data Gateway

Shadow AI risk cannot be eliminated by discovery alone. This post outlined how visibility, classification, and data-in-motion controls combine to stop AI ingestion and exfiltration; accelerate incident response; satisfy regulatory requirements; integrate with DLP, SIEM, CASB, and identity; and sustain governance through audits and user education.

The Kiteworks AI Data Gateway operationalizes these outcomes. It mediates prompts and responses, masks or tokenizes sensitive elements, routes only to approved models, enforces zero-trust access and end-to-end encryption, and captures full chain-of-custody logging—turning DSPM insights into governed AI usage, real-time prevention, and provable compliance at scale.

To learn more about enhancing your DSPM investment to stop shadow AI data sharing, schedule a custom demo today.

Additional Resources

• Brief Kiteworks + Data Security Posture Management (DSPM)
• Blog Post DSPM vs Traditional Data Security: Closing Critical Data Protection Gaps
  
• Blog Post DSPM ROI Calculator: Industry-Specific Cost Benefits
• Blog Post Why DSPM Falls Short and How Risk Leaders Can Mitigate Security Gaps
• Blog Post Essential Strategies for Protecting DSPM‑Classified Confidential Data in 2026