Highlights
Shadow AI is becoming a growing concern for ISVs as developers adopt AI assistants, coding extensions, and external AI integrations without formal approval. Without clear visibility, these tools can expose proprietary code, compromise customer data, and introduce security, quality, and compliance risks. This guide explains how to detect unapproved AI tools across code repositories and developer environments using code scanning, SDK analysis, endpoint monitoring, and network controls. It also explores AI governance best practices, approved-tool inventories, risk-based classifications, and frameworks such as NIST AI RMF and ISO/IEC 42001. Learn how ISVs can strengthen AI security and governance while enabling developers to experiment responsibly.
Artificial intelligence is changing how software gets built. Developers use AI assistants to write code, troubleshoot issues, generate tests, and complete routine tasks faster. Product teams also experiment with AI-powered features to accelerate development.
But what happens when these tools enter the development process without the organization’s knowledge or approval?
A developer might share proprietary code with a public AI assistant. An engineer might connect an application to an external AI service without a security review. A team might install an AI-powered plugin without understanding what information it can access.
These decisions may be well-intentioned, but they can introduce risks that organizations are not prepared to manage.
This is known as Shadow AI. For independent software vendors (ISVs), it raises important questions about intellectual property, customer data, software quality, and compliance. The challenge is to support AI-driven productivity while maintaining control over how tools access company information and product code.
What Is Shadow AI?
Shadow AI refers to the use of artificial intelligence tools, applications, or services without an organization’s knowledge, approval, or appropriate oversight.
Similar to Shadow IT, where employees adopt unapproved software, Shadow AI introduces additional concerns because these tools may process sensitive information, generate code, or influence product functionality.
As explained in IBM’s overview of Shadow AI, unapproved AI usage can create security, privacy, and governance challenges when organizations lack visibility into how these tools are used.
In software development, Shadow AI can take several forms:
- Unapproved AI assistants: Developers use external tools to write or troubleshoot code.
- AI-powered extensions: Plugins access project files, browser content, or development environments without review.
- Unreviewed integrations: Applications connect to external AI services without formal security checks.
- Unapproved experiments: Teams test models using proprietary code or customer information.
Not every instance of Shadow AI creates an immediate security problem. The concern is that organizations may not know what information is being shared, where it goes, or whether the tools meet their security requirements.
Fig: Forms of Shadow AI in Software Development
For ISVs, this lack of visibility matters because source code, product architecture, and customer data are central business assets.
Why Should ISVs Be Concerned About Shadow AI?
ISVs operate in a competitive environment where faster development and continuous innovation are essential. AI tools can support both, but unmanaged adoption can create risks beyond individual employees.
Microsoft and LinkedIn’s 2024 Work Trend Index found that 78% of AI users were bringing their own AI tools to work. This is broader workplace research, not ISV-specific data, but it illustrates how easily AI usage can extend beyond approved systems.
The financial impact of security incidents also deserves attention. IBM’s 2026 Cost of a Data Breach Report puts the global average cost of a data breach at $4.99 million. This figure covers data breaches overall, not Shadow AI incidents specifically, but it provides context for the potential cost of inadequate security controls.
For ISVs, five areas deserve particular attention.
1. Protecting intellectual property
Source code reveals how a product works and what differentiates it from competing solutions. When developers submit code to external AI tools, organizations need to understand how that information is processed and retained.
Why it matters: Uncontrolled sharing can expose proprietary code and create confidentiality or contractual concerns.
2. Keeping customer data secure
Developers often use real examples to troubleshoot problems. These may include error logs, database structures, customer records, or configuration details.
Sharing sensitive information with an unapproved AI service can move it outside the organization’s intended security boundaries.
Why it matters: ISVs may have contractual and regulatory obligations governing customer information. Unapproved AI usage can make these obligations harder to meet.
3. Maintaining software quality and security
AI-generated code can accelerate development, but it is not automatically secure or correct. It may introduce bugs, vulnerable dependencies, or overlooked edge cases.
You also might want to read: The AI Revolution in QA: From Test Automation to Intelligent Quality Engineering – Nitor Infotech Blog
Why it matters: Code produced or modified with AI still needs appropriate review, testing, and security validation before release.
4. Managing compliance and licensing obligations
AI services have different terms for data processing, retention, and usage. AI-generated code can also raise questions about licensing and the origin of software components.
Why it matters: Organizations need to evaluate these factors before adopting tools or introducing AI-generated code into customer-facing products.
5. Avoiding fragmented AI adoption
When individual teams select their own tools, organizations can end up with overlapping subscriptions, inconsistent security controls, and limited visibility into AI usage.
Why it matters: Without a coordinated approach, managing costs, assessing risk, and maintaining consistent development standards becomes more difficult.
Fig: Risk of Shadow Ais for ISVs
How to Detect Shadow AI in Code Repositories
Repository scanning can identify AI-related libraries, integrations, and configuration signals. These findings help teams investigate unfamiliar usage, but they should not automatically be treated as evidence of a policy violation.
1. Scan for AI SDKs, endpoints, and credentials
Code scanning and dependency analysis can flag libraries associated with AI services. Examples include:
- JavaScript and TypeScript packages: openai, @anthropic-ai/sdk, @google/genai, and langchain.
- Python packages: openai, anthropic, google-genai, and langchain.
- External service endpoints: References to api.openai.com, api.anthropic.com, and generativelanguage.googleapis.com.
- Credential indicators: Environment variable names such as OPENAI_API_KEY, ANTHROPIC_API_KEY, and GEMINI_API_KEY.
Google recommends its newer @google/genai JavaScript SDK and google-genai Python package, replacing the older @google/generative-ai and google-generativeai libraries in new implementations. Legacy packages may still appear in existing projects, so their presence should prompt review rather than an automatic alert for unauthorized use.
Teams should also look for files and settings that can reveal AI-assisted development workflows. Examples include AGENTS.md, .cursorrules, Cursor rule files under .cursor/rules/, and MCP server configurations. These can indicate agent instructions, coding-assistant rules, or connections to external tools. Their formats vary by product, and none proves that a tool is unapproved.
Secret-scanning tools can detect exposed credentials, while dependency and code analysis can identify unfamiliar integrations. Comparing the results with an approved-tool inventory helps teams distinguish legitimate usage from activity that needs further investigation.
2. Look beyond the codebase
Not all Shadow AI leaves a visible trace in source code. A developer might use a chatbot through a browser, install an AI-powered coding extension, or sign in with a personal account.
Organizations can extend visibility through existing security controls:
- Mobile device management (MDM) and endpoint management: Inventory installed applications and identify unmanaged devices accessing development resources.
- Browser extension controls: Review installed extensions, restrict unapproved additions, and examine permissions that allow access to company information.
- Proxy and DNS logs: Identify connections to known AI services and investigate them against approved-tool inventories.
- Cloud access security brokers (CASB): Where supported, identify cloud application usage and assess whether data-sharing policies are being followed.
No single control captures every interaction. Encrypted traffic, personal devices, and AI features embedded in approved SaaS products can create visibility gaps.
Lansweeper’s guide to Shadow AI detection discusses asset discovery and inventory as ways to identify tools that traditional software inventories may miss. This is vendor guidance rather than independent research, but it offers a practical starting point.
3. Monitor how AI is used
Some risks only become visible during use. An application may begin sending information to an external AI service, or a previously approved tool may gain new capabilities.
Network monitoring, identity and access logs, and application telemetry can help teams identify unexpected connections or access patterns. Where appropriate, controls can also flag attempts to send sensitive data to external services.
Adaptive Security’s guide to detecting unauthorized AI use discusses combining signals from network, endpoint, browser, and SaaS environments. This is also vendor guidance, so organizations should evaluate the recommendations against their own security architecture.
Repository analysis, developer-environment visibility, and ongoing monitoring work best together. Findings should trigger investigation, not an automatic assumption of policy violations.
For related guidance on coding assistants, see Nitor Infotech’s AI-Assisted Coding: Best Practices for Smarter, More Efficient Software Development.
Discover how security testing helps identify vulnerabilities before they reach production
How Can ISVs Govern Shadow AI Without Slowing Innovation?
Detection establishes visibility, but governance determines what happens next. Effective AI governance defines which tools are acceptable, which require additional safeguards, and which should not be used.
1. Establish clear AI usage policies
Developers need straightforward guidance on which AI tools they can use and what information they can share.
Policies should cover approved tools, restrictions on confidential information, review requirements for AI-generated code, and the process for requesting new tools. They should also specify who is responsible for reviewing exceptions.
2. Maintain an approved-tool inventory
Organizations should document reviewed AI tools and services, including their owners, permitted uses, data access, and security requirements.
An assistant approved for public information may not be suitable for confidential source code or customer data.
You also might want to read: AI Governance for ISVs: A Framework for New Builds and Legacy Modernization – Nitor Infotech Blog
3. Match controls to the level of risk
A simple classification model can help teams make consistent decisions.
- Approved: A company-managed coding assistant used for permitted development tasks, with appropriate access controls and code review requirements.
- Restricted: An external model integration that may process sensitive information but is permitted only after security review, data-handling checks, and explicit approval.
- Prohibited: An unreviewed public tool used to upload production customer records, private keys, or confidential source code where policy or contractual obligations forbid that disclosure.
Classification should reflect the tool, the data involved, the intended use, and the permissions granted. A tool should not automatically be considered safe or unsafe in every context.
4. Align governance with recognized frameworks
ISVs can use established frameworks to structure their approach to risk and compliance.
- NIST AI Risk Management Framework (AI RMF): Helps organizations identify, assess, and manage AI-related risks throughout the AI lifecycle.
- ISO/IEC 42001: Provides requirements for establishing and continually improving an AI management system.
- EU AI Act: Establishes legal obligations for applicable AI systems and organizations, depending on the system’s role, risk category, and activities.
These frameworks serve different purposes and are not interchangeable. Not every requirement applies to every ISV. Organizations should determine which standards and legal obligations are relevant to their products, customers, and markets.
Useful starting points include the NIST AI RMF, ISO/IEC 42001, and the European Commission’s AI regulatory framework.
5. Build security checks into the development lifecycle
When developers introduce new AI services, security reviews can assess the integration. Automated checks can flag exposed credentials or unfamiliar dependencies, while code reviews and testing help validate AI-generated changes.
You also might want to read: Introduction to ADLC – Nitor Infotech Blog
6. Make responsible experimentation practical
Developers are more likely to follow policy when approved alternatives meet their needs and requesting a new tool is straightforward.
Engineering and security teams should work together to evaluate promising tools, document acceptable use, and establish safeguards before wider adoption. This supports experimentation while keeping decisions accountable.
Measuring the Effectiveness of AI Governance
Organizations should regularly assess whether their approach is improving visibility and reducing risk.
Useful measures include:
- Coverage: What proportion of relevant repositories, endpoints, and development environments are monitored?
- Confirmed unapproved usage: How many cases have been investigated and verified?
- Review time: How quickly are newly identified tools assessed?
- Remediation: How many confirmed issues have been resolved?
- Policy compliance: Are developers following established requirements?
Metrics need context. An increase in detected Shadow AI may indicate better monitoring rather than worsening security. The objective is to improve visibility, prioritize meaningful risks, and strengthen controls over time.
Fig: Measures of AI Governance Effectiveness
Moving Toward Responsible AI Adoption
Shadow AI reflects how quickly AI tools are entering everyday development. Developers want to solve problems faster, product teams want to experiment, and businesses want to capture the benefits.
For ISVs, effective management starts with knowing which tools are being used, what information they can access, and whether their use meets organizational requirements.
Repository scanning, endpoint and browser controls, network monitoring, and an approved-tool inventory provide a practical foundation. Clear policies, risk-based reviews, and lifecycle security checks help turn those findings into action.
If your engineering teams are expanding their use of AI, review your current detection and governance approach before the next tool or integration reaches production.
Contact Nitor Infotech to discuss how to strengthen security, quality, and governance across your software development lifecycle.
Accelerate your product development lifecycle with Nitor Infotech’s Quick Dev with AI cheat sheet, featuring practical AI-powered PDLC best practices, considerations, common pitfalls, and prompts for coding, testing, and reviews.
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Key Takeaways
- Shadow AI can introduce hidden risks: Unapproved AI tools may expose proprietary code, customer data, and sensitive information.
- Detection requires multiple layers: Combine repository scanning, dependency analysis, secret detection, endpoint controls, and network monitoring to identify unauthorized AI usage.
- Clear policies make a difference: Maintain an approved-tool inventory and define which tools can be used, what data can be shared, and when additional review is required.
- Governance should be risk-based: Use frameworks such as NIST AI RMF and ISO/IEC 42001 to guide AI risk assessment, accountability, and compliance.
- Security and innovation can coexist: Give developers access to approved AI tools and integrate security checks into the development lifecycle to support responsible experimentation without slowing delivery.
