TL; DR AI Governance Compliance for Lean IT Teams
- An AI acceptable use policy alone isn’t enough. Auditors expect controls to be enforced in real time.
- Five core controls can help you prove AI governance, even without a dedicated security team: Discovery, Classification, Authentication, Governance, and Auditability.
- Frameworks like SOC 2, HIPAA, GDPR, ISO 27001, ISO 42001, NIST AI RMF, and the EU AI Act all require some form of access governance evidence.
- The biggest blind spots for small to mid-sized teams are personal AI accounts and shared credentials, which undermine user attribution.
- If you've just begun discussions on governing AI access, start with this guide: Allow, Warn, Block: A Practical AI Governance Model for Lean Teams. Then, return to this article to build controls that pass auditor scrutiny.
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This guide explains:
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Everyone talks about governing ChatGPT.
But first things first: What about how it’s accessed?Many teams are surprised when they fail an AI governance audit. After all, they have an acceptable use policy. How could they fail an audit?
...the risk landscape is becoming significantly more complex as systems move toward autonomy...
To address AI risks, many organizations have implemented governance frameworks built on policies, ethical guidelines, and oversight committees. While these efforts demonstrate intent, they are increasingly out of sync with the pace of its evolution.
Teams may comply with governance requirements yet still expose the organization to significant risk (from “The New AI Risk Reality Check”)
In other words, there's a big difference between having a policy and being able to show the policy applies in real-time, in sync with the pace of change.
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About the expert in this article: Andreas Welsch
Andreas Welsch is the Founder & Chief AI Strategist at Intelligence Briefing and the best-selling author of two books, the AI Leadership Handbook and The HUMAN Agentic AI Edge.
For over two decades, Andreas has advised Fortune 500 leaders on AI value realization and led enterprise-AI integrations at scale. In this article, he applies enterprise AI governance to help smaller orgs with lean IT resources navigate compliance.
Outside of work, Andreas is an Adjunct Professor at West Chester University of Pennsylvania and serves on the Editorial Board of the Journal of AI, Robotics, and Workplace Automation.
He’s also a frequent keynote speaker and has been named a LinkedIn Top Voice, a Top 10 Thought Leader in AI and Agentic AI, and a Top 30 AI Leader. Andreas has been featured on CIO.com, VentureBeat, Forbes, & CNBC. |
According to Kaseya's analysis of 27.6 billion+ SaaS security threats across 50,000+ SMBs, unmanaged guest accounts now make up 69% of accounts in SaaS environments.
The threat surface is growing.
So, orgs with acceptable use policies can still fail audits if they lack visibility into AI usage.
The solution isn’t more policies but implementing the controls that make AI access visible (i.e. tying activity to named users), enforcing governance at login, and generating audit evidence automatically.
This week, I talked to Andreas about the key governance controls that create the evidence trail auditors want. And I promise, they’re realistic for a lean team to maintain.
What’s the difference between an AI governance policy and AI governance controls?
First, let’s get definitions out of the way. An AI governance policy defines the rules, while AI governance controls enforce them (with clear evidence for it).
A policy tells employees which AI tools are approved, how to handle data, and what's prohibited.
But a policy alone doesn't tell an auditor:
- Which tool was accessed and by whom
- The date of access
- Which credentials were used
- The type of authentication (SSO versus username/password)
Consider two common failure modes:
- When an employee logs in to an AI tool with a personal Gmail on a corporate device, attribution is unclear.
- When three employees share access to an AI writing platform, you can't tell an auditor who accessed what, when, or how.
Together, these scenarios show why discovery and authentication are the controls you must prioritize.
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Which compliance frameworks will ask for AI governance evidence?
If your employees use AI tools to process PII, corporate, or regulated data, auditors will look for:
SOC 2: Logical access controls & user accountability
HIPAA: Authentication, access control, audit controls
GDPR: Records of tools processing personal data
ISO 27001: Asset inventory, monitoring & access controls
NIST AI RMF: AI inventory, governance, and risk management
EU AI Act: Logging, monitoring, and risk-based oversight
ISO/IEC 42001: AI management system controls
The common thread: Access governance (inventory, authentication, monitoring, and audit evidence).
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At minimum, what AI governance controls should you prioritize?
At minimum, the governance controls you should prioritize are Discovery, Classification, Authentication, Governance, and Auditability.
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Control
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What it does
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Core compliance need it addresses
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#1 Discovery
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Identifies every AI tool in use, whether accessed with corporate or personal credentials
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AI tool inventory
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#2 Classification
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Assigns risk tiers to AI tools
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Risk-based classification of assets (formal risk classification categories are required for EU AI Act compliance)
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#3 Authentication
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Links AI access to specific, authenticated users
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User-level accountability
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#4 Governance
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Enforces access policies in real-time, at login
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Controlled access to regulated data
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#5 Auditability
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Produces evidence (logs, records, and reports) to demonstrate controls are working
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Compliance evidence on demand
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Control #1 (Discovery): What counts as an AI tool for compliance purposes?
For compliance purposes, an AI tool is any app your employees use that processes corporate or client data, even if it’s outside IT oversight.
A quick glance at the most popular AI tools in the corporate workspace
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Type of AI tool
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Examples
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How employees are typically accessing it
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Main business risk
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Chatbots
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Perplexity, Claude, ChatGPT, Gemini, Microsoft Copilot
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Often accessed with personal email credentials; corporate SSO available on paid tiers
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Data leakage & policy violations
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Writing assistants
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Grammarly, Jasper, copy.ai
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Often accessed with personal emails
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Exposure of confidential material, language that creates liability
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Code-writing assistants
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Base44, Replit, Cursor, GitHub Copilot
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Mix of personal and corporate credentials; team API key common
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Insecure code generation, intellectual property or open-source license conflicts
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Meeting and note assistants
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Otter, Fireflies, BlueDot
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Personal emails almost always used
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Exposure of confidential conversations
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HR & recruiting tools
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Beam AI, hireEZ, Eightfold AI, Harver
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Corporate credentials but may not be SSO-protected
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Hiring bias & legal exposure
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Design generators
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Canva, Adobe Firefly
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Often accessed with personal emails
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Accidental uploads of unreleased assets, designs mimic copyrighted elements or trademarked styles
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Analytics and business intelligence (BI) tools
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Microsoft 365 Copilot, Gemini in Google Sheets, AI in Tableau
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Usually accessed with corporate credentials
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Exposure of financial or operational data, bad decisions due to flawed analytics
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Perplexity Comet, Opera Neon, Gemini Auto Browse, Samsung Browser for Windows
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Often accessed with personal credentials, bypassing corporate controls
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Unauthorized actions, credential exposure
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You can't prove compliance for AI tools you don't know exist. That's why visibility is the first and most important AI governance control.
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[Visibility] starts with a clear and actionable AI governance policy that employees can actually understand and apply in their day-to-day work. It should outline what tools are approved, what types of data can be used, and when additional review is required. Without this clarity, employees will make decisions on their own, often without fully understanding the implications ~ Andreas Welsch, founder and Chief AI Strategist, Intelligence Briefing
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What Discovery looks like in practice
A complete AI tool inventory accounts for both corporate and personal logins.
This means every tool or app your team is accessing from work devices, including those done with personal Gmails.
At an ISC2 spotlight session on AI (that I attended this morning), one speaker shared about helping a healthcare tech company run its first AI inventory.
The company found roughly 80 agents that had spun up inside a sanctioned tool in Sales, unmonitored and drifting from their original use. And that’s just one department, for an approved tool IT already knew about.
Imagine how much harder it is to assess risk when employees adopt tools outside IT visibility altogether.
At minimum, your inventory should show the tool/app name, email address used for logins, and date of last access. These three data points answer important first questions: What’s being used? Who’s using it? And is it still active?
Without those answers, it’s difficult to assess risk, review access, or demonstrate governance.
Control 2 (Classification): How do you decide which AI tools to allow, warn, or block?
You classify AI tools based on a defined set of risk attributes.
Two tools in the same category can have very different risk profiles, depending on their data handling practices, login security, and deployment model.
Six questions to ask when classifying an AI tool
1. Data retention and use of training data: Does the vendor use your prompts to train its models? And do they retain your data after the session ends, and if so, under what terms? Be wary if the vendor's default terms allow prompt data to train models, with opt-out buried in enterprise settings.
2. Availability of Data Processing Agreements (DPA): If your organization handles EU personal data, a tool without a DPA can’t be approved under GDPR Article 28. But if your organization doesn't operate under GDPR, document vendor data handling terms in its place.
3. Secure authentication support: Does the tool support SSO? No SSO option means you can't enforce MFA at the identity layer.
4. OAuth scope: If a tool connects to your Google Workspace or Microsoft 365, what permissions does it request? A meeting summarizer that requests read/write access to your entire email inbox is asking for far more than it needs. The wider the permissions, the higher the risk tier.
5. Default deployment model: Is the tool publicly accessible without authentication to view it? AI coding platforms that deploy apps publicly by default are a distinct risk category, not just for the tool itself but for any data passed through it.
6. Vendor security posture: Does the vendor publish a SOC 2 report or any security documentation? Security posture is worth considering as part of classification.
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Classification made easy for lean teams
Andreas Welsch, founder & Chief AI Strategist at Intelligence Briefing, recommends:
The answers inform whether an app is ranked a low, high, or unacceptable risk.
As Andreas puts it, “Keeping the online form lean and review timeline short demonstrates an organization is taking governance seriously without putting up hurdles to innovation.”
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Tools that raise red flags for #1, #3, and #5 should default to Warn or Block:
- Allow (for approved apps)
- Warn (accessible with a policy reminder at login)
- Block (restricted at the point of access)
For how to apply those tiers in practice, see Allow, Warn, Block: A Practical AI Governance Model for Lean Teams.
Control 3 (Authentication): Why is authentication an AI governance compliance control?
Authentication is a compliance control because it determines whether access to AI tools can be attributed to a specific user.
Where individual accounts aren't available, document access rules explicitly.
At a minimum, your shared access record should capture:
- Which employees are authorized to use the account
- The business reason for shared rather than individual access
- The review frequency (quarterly is defensible for most frameworks)
- Any access changes made since the last review
This documented record is the closest substitute for user-level attribution, and auditors will accept it if it's maintained consistently.
The stakes are high. At the same ISC2 spotlight event on AI, a speaker shared a hair-raising story of a developer changing teams, with his access permissions intact.
Shortly after he left, his coding agent used his credentials to access data in another jurisdiction. And that’s not all. The cross-border transfer involved human genetic research data.
So, do you blame the developer, agent, or the lack of an access review? The jury’s still out on that one, but one thing’s clear: Without user-level attribution, you can’t even ask the right questions, let alone answer them.
But what about MFA?
Under SOC 2 CC6.1, MFA is explicitly listed as a point-of-focus control for logical access.
This means auditors are specifically trained to look for enforcement evidence, not just deployment.
Under HIPAA §164.312(d), person or entity authentication is required, and MFA enforcement logs are a standard way to satisfy it.
Control 4 (Governance): How do you enforce AI access policy at the point of access?
Governance means enforcing your policy at the point of login, with a lightweight and agile user experience.
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If the review requires several rounds, drags on for multiple weeks or months, and the final answer is “the risk is too high,” the process becomes a burden rather than an effective governance tool. Users will look for ways to sidestep the process, which will further diminish security and governance ~ Andreas Welsch, founder and Chief AI Strategist at Intelligence Briefing
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So, the most practical enforcement model for lean IT teams is identity-layer governance: controls applied in the browser, at the point of login.
- When an employee tries to access a tool classified as Block, they see a “block” screen in the browser with a customized message directing them to an approved alternative.
- When a tool is classified as Warn, they see a policy reminder before proceeding.
For a full breakdown of how to assign tiers, see Allow, Warn, Block: A Practical AI Governance Model for Lean Teams.
Access reviews as a governance control
Point-of-access controls provide real-time security.
But periodic access reviews are just as important, especially when permissions shift or data handling terms change.
A quarterly access review doesn't need to be complicated. At minimum, cover:
- Which tools are currently at each tier
- Which have changed their security posture or terms since the last review
- Whether any access rights have changed
Document what you reviewed, your findings, and any changes you made.
- For a quarterly review template, use three columns: Tool, Current Tier, and Change Since Last Review.
- Add a fourth column if any access rights changed, to account for who made the changes, when, and why.
That export is irrefutable, ongoing proof of governance.
In LastPass, your SaaS Monitoring inventory and SaaS Protect audit logs give you the data to run this review and export the record as compliance evidence.
Control 5 (Auditability): What evidence does an auditor actually need for AI governance?
An auditor needs to see AI access tied to specific users, and that access controls are working as intended.
The table below maps each framework's requirements to the specific evidence artifact and how LastPass helps produce it.
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Framework
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What an auditor needs
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How LastPass produces it
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SOC 2 CC6.1–CC6.3
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Evidence logical access controls are applied consistently and operate effectively
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Admin console reporting surfaces risky app activity and authentication gaps by severity
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HIPAA §164.308–164.312
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Evidence of access governance, user accountability, audit controls, strong authentication for systems that may process ePHI
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SaaS Monitoring user logins (via personal or corporate credentials), SaaS Protect enforcement logs, MFA authentication assurance *
*for protecting workstations, Active Directory, & on-prem LDAP services*
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GDPR Articles 28–30
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Visibility into AI/SaaS apps used for processing personal data & support for maintaining DPAs (data processing agreements) and ROPAs (record of processing activities)
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SaaS Monitoring surfaces AI & SaaS apps that may process personal data, helping validate DPA coverage & improve ROPA accuracy)
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ISO 27001 A.5.9 & A.8.16
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Discovery, inventory, access attribution, and monitoring of AI-enabled SaaS apps
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SaaS Monitoring live inventory; access attribution records via admin audit trail
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NIST AI RMF GOVERN 1.6, MAP 4.1/4.2
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Inventory lifecycle management; ongoing monitoring, user attribution, & governance of third-party AI systems
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Govern 1.6: SaaS Monitoring inventory of AI-enabled apps
Govern 4.1/4.2: SaaS Monitoring inventory of open-source third-party AI systems
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The EU AI Act Article 4 & Article 26
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Evidence measures exist to inform staff of AI governance policies; access monitored and attributable for high-risk AI systems (e.g. HR screening or hiring tools)
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Article 4: SaaS Protect exportable Warn and Block logs for reinforcing AI access literacy
Article 26: SaaS Monitoring login-level access attribution for high-risk AI tools; SaaS Protect for access policy enforcement *
*Note: Art. 26(6) requires retaining direct operational logs of the AI system itself. These must be exported from the AI tool's own admin console*
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ISO/IEC 42001 Clause 7.5
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Inventories, monitoring & access governance records supporting an AI Management System (AIMS)
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SaaS Monitoring inventory of AI-related SaaS apps; SaaS Protect exportable governance and audit logs retained as documented information within an AIMS
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What an audit-ready evidence package looks like
When an auditor asks about AI governance, you should be able to produce:
- An AI tool inventory record
- Named-user access records
- MFA enforcement records
- AI classification decisions
- Allow/Warn/Block enforcement records
- Access review documentation
- Audit logs covering the review period
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Quick self-check: If you can answer yes to all three, your controls are in good shape.
Discovery and authentication are where lean IT teams are most exposed. Both are addressable without enterprise-scale tools.
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How does LastPass provide the AI governance controls you need to prove compliance?
While no single tool can meet all compliance requirements, LastPass delivers the core secure access controls many frameworks expect:
- AI app discovery in the browser
- Enforced security policies at login
- Evidence that controls are working consistently and effectively
Because every AI governance framework ultimately requires visibility into who accessed which systems and under what conditions, identity-layer controls are often the foundation supporting broader compliance efforts.
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LastPass Capability
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AI Governance Control
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Governance Outcome
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Evidence Artifact
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Discovery
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Continuous AI/SaaS tool inventory, accessed with either personal or corporate credentials
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Discovered Apps export
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Classification + Governance
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Allow/Warn/Block enforcement at login based on risk tier
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Allow, Block and Warn logs
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SSO + MFA
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Authentication
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Identity-based access to AI tools with enforcement evidence
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MFA enforcement records
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Shared access management (vault)
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Authentication
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Controlled, user-attributable access to shared AI accounts
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Access attribution records
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Security dashboard
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Auditability
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On-demand compliance evidence across all five controls
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Exportable access & authentication reports
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Sources
