Manage an employee-built app made with AI like any other software: give it a named owner, assess what it can access and affect, review and test its code, and monitor it after launch. AI authorship and an employee’s coding experience do not determine whether an app is safe. The right controls depend on its data, permissions, audience, and potential consequences.
Start with an approved path and an app inventory
Make it possible for employees to build useful tools without letting untracked apps become shadow production systems. Define approved AI services, development environments, and hosting options, then require a lightweight intake record before an app is deployed or connected to organizational data.
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Record the app’s purpose, business sponsor, accountable owner, intended users, hosting location, data handled, external services, AI tools used, dependencies, permissions, and whether AI runs inside the app or the app can take actions. The inventory should be kept current as the app changes. NIST’s Generative AI Profile identifies governance practices such as monitoring, incident response, auditing, and decommissioning as relevant risk-management considerations.
Set review depth by risk, not by who wrote the code
Use existing organizational risk tiers if they cover the app’s risks; adapt them where generative AI introduces additional concerns. NIST’s Generative AI Profile says organizations may apply existing tiers or revise them and notes that additional human review, documentation, tracking, and management oversight may be warranted.
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Consider the factors below together rather than relying on a made-up numerical score or universal approval threshold:
- Data: How sensitive is it, and how much of it can the app access?
- Exposure: Is the app limited to a small internal group, broadly available, or exposed externally?
- Consequence: Could an error affect people, business-critical work, or important records?
- Authority: Can it edit or delete records, spend money, or send communications?
- Assurance: Are testing, logging, rollback, and ownership arrangements adequate?
Route higher-impact apps to the relevant security, privacy, legal, or engineering reviewers. Applicable legal obligations depend on the app’s geography, industry, data, and function; the app’s AI origin alone does not establish which rules apply.
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Protect information sent to AI services and used by the app
Control what employees put into prompts
Specify which AI tools are approved and what information may be submitted to them. Microsoft’s Windows development security guidance advises against putting API keys, passwords, connection strings, real customer details, or other personal information into prompts. Employees should check organizational policy before sharing proprietary code. Use synthetic data for examples rather than real customer or employee records.
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Limit the app’s data access
Give the app only the data and permissions needed for its purpose. Set appropriate authentication and authorization, and avoid broad access when a narrower role- or group-based grant will work. Establish how the app handles and retains data, including data sent to external services. Microsoft’s organizational AI governance guidance describes role- and group-based access controls as one way to reduce insider risk.
Rank #3
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Apply secure-development checks to generated code
Generated code is not exempt from review. Microsoft’s guidance states: “AI tools don’t remove the need for code review. They change what you’re reviewing, not whether you review.” Require a human reviewer to understand the changes and test the app at least as thoroughly as comparable hand-written software.
- Validate inputs: Check type, length, and range. Do not pass raw user input into shell commands, file paths, or database queries; ensure resolved file paths stay within permitted directories.
- Reduce capabilities: Review what the app can read, change, execute, and access, then remove permissions it does not need.
- Review dependencies: Check each suggested package’s publisher and maintenance status, and scan dependencies for known vulnerabilities. For a NuGet project, Microsoft documents
dotnet list package --vulnerableas one ecosystem-specific example, not a universal scanner. - Protect secrets: Keep credentials out of source code and prompts; store them in an appropriate secrets-management service or vault.
- Test failure cases: Include malformed, unexpected, and boundary inputs, and verify that errors do not expose data or grant unintended access.
NIST SP 800-218A adds AI-specific secure software development practices to NIST’s Secure Software Development Framework (SP 800-218). It is lifecycle guidance for producers of AI models and systems and their acquirers; it is not a requirement that every employee implement the publication personally.
Rank #4
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- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
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Separate AI-assisted development from AI behavior at runtime
An app can be written with AI help without using AI when it runs. If it calls a model or agent at runtime, document its purpose, data flows, boundaries, and expected behavior. Test representative and edge-case inputs, consider whether users should be told about material AI use, and keep people involved in consequential decisions. Microsoft advises against allowing an app to autonomously delete data, make purchases, or send messages without explicit confirmation.
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If an app’s AI can invoke tools or APIs, treat those capabilities as authority to act—not merely as another input field. Microsoft’s agent security guidance identifies risks including unintended actions, sensitive-data leakage, supply-chain compromise, and unmanaged or over-permissioned agents. Apply controls such as:
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- Grant only the tools, data, and permissions required for the stated task.
- Block prohibited actions with deterministic controls rather than relying on a prompt alone.
- Require human approval for high-risk or irreversible actions.
- Show the intended action before approval, and provide a reliable way to pause or stop the app.
- Keep accessible logs of tools called, data involved, approvals, and outcomes so an incident can be investigated.
Monitor, respond, and retire apps throughout their life
Assign someone to monitor use, permissions, dependencies, incidents, and material changes. Define who handles a suspected data exposure or unsafe action, how access can be revoked, and how the app can be taken offline or rolled back. Reassess the app when its data, audience, integrations, permissions, or behavior changes.
Periodically confirm that the owner and business need still exist. When an app is no longer needed, remove it and revoke associated credentials and integrations. NIST’s Generative AI Profile includes monitoring, change management, incident response, and decommissioning among governance considerations.
Use governance guidance as a framework, not a substitute for judgment
The NIST AI Risk Management Framework is voluntary guidance, and NIST states that it is being revised. Its Generative AI Profile was released on July 26, 2024. The AI-specific secure development supplement, NIST SP 800-218A, complements rather than replaces SP 800-218; NIST’s announcement records an update on June 25, 2025. These documents can inform a company’s process, but the controls for an individual app should still match its actual data, exposure, and potential impact.
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