AI tools risked data leaks; single sign?on (SSO) allowlist with secure web gateway, DLP, and legal case workflow

Overview

AI tool usage policies existed on paper but were not enforced in the apps people used. Employees pasted source code, customer data, and internal roadmaps into unapproved AI tools, while approved tools lacked consistent data loss prevention (DLP) controls. Legal learned about incidents from support tickets after content had already left the environment. Intelligex connected DLP and access controls to an allowlist of approved AI tools, enforced blocking and redaction for sensitive content, stood up an incident review workflow that routed exceptions to Legal, and required legal approvals for onboarding new AI tools. Policy moved into the path of work, risky data exposure dropped, and audits drew from a single case record—while the client’s identity provider, secure web gateway, CASB/SASE, SIEM, and collaboration tools stayed in place.

Client Profile

• Industry: Enterprise software and services handling regulated customer and workforce data
• Company size (range): Global workforce with engineering, sales, and operations using web and desktop AI tools
• Stage: Published AI policy; ad hoc guidance by team; no technical enforcement; manual incident handling; inconsistent vendor onboarding
• Department owner: Legal & Compliance (Privacy, IP, and Legal Operations)
• Other stakeholders: Security/GRC, IT/Identity, Data Protection, Procurement/Vendor Risk, HR, Engineering/Data Science, Internal Audit

The Challenge

Employees adopted AI tools faster than controls could keep up. Some teams used browser-based chat tools, others tested SDKs and extensions, and a few groups worked with enterprise AI offerings. The policy said not to paste sensitive data, but tooling did not enforce it. DLP covered email and storage but ignored outbound posts to AI endpoints. Developers and analysts used personal accounts, making it impossible to see what left the network or to apply retention and discovery when incidents arose.

Incidents were handled inconsistently. When a manager spotted risky content in an AI prompt, they asked IT to block the site. Exceptions for approved projects lived in email. Legal had no visibility into what was blocked, what was allowed with conditions, or how exceptions were granted. Vendor onboarding lacked a defined legal step, so procurement accepted click-through terms that conflicted with the company’s data handling requirements.

Evidence and governance were fragmented. Logs lived in multiple systems, and incident documentation sat in spreadsheets. The identity provider enforced SSO to enterprise SaaS, but AI tools were accessed directly on the web. The same questions recurred—what counts as personal data, can code be pasted, is the model allowed to retain prompts—and answers varied by handler and urgency.

Why It Was Happening

Policy was not connected to enforcement points. The organization had a clear AI usage policy, but the secure web gateway, CASB/SASE, and endpoint agents did not apply DLP rules to AI traffic. Without an allowlist tied to identity and device posture, unapproved tools slipped through, and approved tools lacked consistent controls.

Onboarding and exceptions lacked a governed path. New AI tools entered through purchase cards and pilots, and legal review happened late. Exceptions and approvals were recorded in email, not in a system that linked choices to risk controls. Incidents were tracked locally, so trends and repeat issues were hard to see and address.

The Solution

Intelligex implemented an AI usage control plane that integrated DLP and access controls with an allowlist of approved AI tools, established incident review and escalation workflows, and required Legal approval for onboarding new tools. Traffic to approved AI domains used enterprise accounts with SSO, contextual banners reminded users of allowed data categories, and DLP policies blocked or redacted sensitive data patterns in prompts and file uploads. Unapproved destinations were blocked with a self-service request path that created a vendor intake record. Incidents flowed into a case system with evidence, reason codes, and legal sign-off. Governance aligned to the NIST AI Risk Management Framework, DLP controls mapped to enterprise policy and tooling such as Microsoft Purview DLP, and identity enforced SSO via providers like Okta. For sanctioned enterprise AI, the platform connected to provider controls (for example, private endpoints or tenant settings) to limit retention and sharing in line with vendor docs such as Azure OpenAI Service.

• Integrations: Identity provider (SSO/MFA) for enterprise accounts; secure web gateway/CASB/SASE for URL allow/deny, tenant restrictions, and inline DLP; endpoint DLP for desktop apps; SIEM for log centralization; ticketing/case system for incidents and approvals; procurement/vendor risk for onboarding; collaboration tools for user notifications.
• Allowlist and routing: Approved AI tools and endpoints with tenant restrictions; sanctioned modes (browser, desktop, API) by role; contextual banners with data handling reminders; blocked destinations with self-service request path.
• DLP and redaction: Patterns for source code, secrets, personal data, customer identifiers, and confidential project terms; inline block or redact actions; exception workflows for approved sandboxes; audit of prompts and file uploads in approved tools.
• Incident workflow: Standardized forms, evidence capture, and reason codes; auto-enrichment with user, device, and data type; legal and Security review gates; maker-checker for severe incidents; remediation tasks and user coaching.
• New tool onboarding: Intake checklist for data flows, retention, sub-processors, and controls; Legal approval for terms, data processing, and IP; Security review for technical controls; pilot scope and monitoring; catalog update upon approval.
• Dashboards and reporting: Usage by tool and role; blocks vs allowed with conditions; incident trends and root causes; onboarding pipeline; exportable packets for Audit with logs, decisions, and remediation.
• Security and privacy: Role-based access to cases and logs; counsel-only notes for privileged analysis; minimal personal data in notifications; immutable logs and retention aligned to policy.

Implementation

• Discovery: Mapped current AI usage by role and channel (browser, desktop, API); inventoried policy and DLP coverage; reviewed identity/SSO posture and tenant settings; sampled incidents and vendor contracts; gathered Legal, Security, Procurement, and Audit requirements for evidence and approvals.
• Design: Defined the allowlist and tenant restrictions; authored DLP patterns and actions by data class; designed incident forms, reason codes, and review gates; planned SSO enforcement and device posture checks; outlined onboarding workflow and intake criteria; set dashboards and audit exports; established change control for allowlist, patterns, and templates.
• Build: Configured secure web gateway/CASB policies, tenant restrictions, and inline DLP; enabled endpoint DLP for sanctioned desktop apps; wired SSO and enterprise accounts; implemented incident queues and approval workflows; connected SIEM for unified logs; built onboarding forms and notifications; instrumented dashboards and access controls.
• Testing/QA: Ran in shadow mode to observe usage and tune patterns; validated blocks, redactions, and banners across tools; simulated incidents and legal reviews; piloted with engineering and marketing groups; tuned thresholds, messages, and allowlist entries from user feedback.
• Rollout: Enabled allowlist and banners first; turned on block/redact for highest-risk data classes; expanded coverage to desktop apps and APIs; activated incident workflows and legal approvals for onboarding; retired ad hoc blocks and email-based exceptions after stable cycles.
• Training/hand-off: Delivered quick guides and just-in-time banners for users; trained Legal and Security on case review and reason codes; briefed Procurement on intake and approval steps; updated policy and FAQs; transferred ownership of allowlist, DLP patterns, and dashboards to Security and Legal Ops under change control.
• Human-in-the-loop review: Established regular calibrations on false positives, tool requests, and incident patterns; recorded decisions with rationale and effective dates; updated patterns, allowlist, and onboarding criteria accordingly.

Results

Policy moved from a document to an enforced experience. Users accessed approved AI tools with enterprise accounts, saw reminders before they shared content, and hit guardrails when prompts contained sensitive data. Unapproved tools were blocked with a clear path to request access, and legal and Security saw the same incident record with evidence and rationale.

Risky exposure decreased and oversight improved. Sensitive content was blocked or redacted inline, exceptions carried reason-coded approvals, and new tools went through a consistent legal and Security review before production use. Dashboards showed where coaching was needed, and audits received complete packets with logs, decisions, and remediation steps. Core platforms remained; the new layer connected identity, DLP, case management, and vendor onboarding under one governance model.

What Changed for the Team

• Before: Users tested any AI tool they found. After: An allowlist with SSO enforced approved tools and blocked others with a request path.
• Before: Policy reminders lived in wikis. After: Contextual banners and inline DLP guardrails met users in the app.
• Before: Incidents were handled in email. After: A case system captured evidence, reason codes, and legal/Security approvals.
• Before: New tools slipped in through pilots. After: Legal-approved onboarding with Security controls preceded production use.
• Before: Logs were scattered. After: SIEM and dashboards showed usage, blocks, incidents, and trends in one place.
• Before: Exceptions were informal. After: Time-boxed exceptions with maker-checker and recorded rationale.

Key Takeaways

• Put policy in the traffic path; pair SSO and allowlists with inline DLP for AI tools.
• Guide and guard; show reminders and block or redact risky content instead of relying on memory.
• Make incidents reviewable; standardize evidence, reason codes, and legal/Security approvals.
• Onboard with intent; require Legal and Security sign-off before new AI tools reach production.
• Instrument everything; centralize logs and show trends so coaching and tuning are targeted.
• Integrate, don’t replace; keep identity, DLP, CASB, SIEM, and ticketing—add orchestration and governance between them.

FAQ

What tools did this integrate with? The control plane used the existing identity provider for SSO and MFA, the secure web gateway/CASB/SASE for tenant restrictions and inline DLP, endpoint DLP for sanctioned desktop apps, a SIEM for logging, and the ticketing/case system for incident review and approvals. Governance aligned to the NIST AI Risk Management Framework, DLP patterns mapped to enterprise policy using controls such as Microsoft Purview DLP, and sanctioned enterprise AI tools used provider controls (for example, Azure OpenAI Service tenant settings).

How did you handle quality control and governance? The allowlist, DLP patterns, and onboarding criteria lived under change control with Legal and Security as owners. Every block, redaction, exception, and approval wrote to immutable logs. Maker?checker applied to severe incidents and long-lived exceptions. Monthly calibrations reviewed false positives, tool requests, and incident themes with decisions recorded and effective dates applied.

How did you roll this out without disruption? Controls ran in monitor mode first to understand usage and false positives. The allowlist and banners went live next, followed by block/redact for the highest?risk data classes. Desktop apps and APIs were enabled after web coverage stabilized. Email-based exceptions and ad hoc blocks were retired after the case workflow and dashboards proved consistent.

How were false positives and developer workflows handled? Patterns were tuned per role and tool, and approved sandboxes used relaxed controls with logging and time?boxing. Developers received guidance on treating code and secrets, and exceptions required legal/Security approval with rationale. Calibration reviews adjusted thresholds and patterns based on real cases.

What did onboarding a new AI tool require? Requesters completed an intake on data flows, retention, sub?processors, and controls. Legal reviewed terms, data processing, and IP; Security validated technical controls and tenant settings; and a pilot ran with monitoring before catalog approval. Approved tools were added to the allowlist with specific modes and DLP patterns.

How did you protect privacy and privilege in incidents? Case records used role?based access with counsel?only notes for privileged analysis. Notifications carried minimal details and linked back to the system. All access and exports were logged, and retention followed records policy and legal hold where applicable.

Can this support mobile and BYOD? Yes. Access to approved AI tools required SSO and device posture checks where possible. For unmanaged devices, web access ran through the secure web gateway with tenant restrictions and inline DLP. Exceptions were limited and recorded with time bounds and rationale.

How did you educate users without slowing them down? Just?in?time banners and short guides appeared at the moment of use. Blocks provided a reason and a link to request access or learn acceptable alternatives. Dashboards identified teams that needed targeted coaching rather than broad reminders.