Shadow AI: Securing the Tools Your Teams Are Already Using

AI adoption rarely waits for a policy review. An employee pastes client information into a public chatbot to save time. A team switches on an AI feature connected to business files. A developer creates an agent with access to internal systems, but no one has reviewed what it can reach or do.

These are not distant possibilities. They are practical governance and security questions for organisations already using AI, often across departments and suppliers. The issue is not whether your people will use AI. It is whether your organisation understands and manages the exposure.

Banning AI outright can drive use further out of sight and remove genuine productivity benefits. But “we didn’t know” is not a defensible answer when clients, insurers and regulators ask how AI is being used and protected. Your board needs a documented response.

Shadow AI creates risks your organisation may not see

Shadow AI describes AI tools, features, applications or agents used without adequate organisational visibility, approval or oversight. It can include a public service accessed by an individual, an AI feature added to existing SaaS software, or a custom application built by a team.

The UK National Cyber Security Centre (NCSC) advises organisations to focus on reducing the risks of shadow AI, rather than assuming it can simply be eliminated. Its guidance on the hidden risks of shadow AI also emphasises the value of understanding why staff turn to unapproved tools and offering safer alternatives.

Company and client data can leave through the front door

Prompts may include personal data, client records, commercially sensitive information, credentials, source code or confidential plans. Once information is sent to an external service, your organisation needs to know how the provider handles it, including retention, access and any use in service improvement.

This is not only a technical concern. Uncontrolled processing can raise data protection, contractual, confidentiality and intellectual property issues. The Information Commissioner’s Office (ICO) guidance on AI governance and accountability is a useful reminder that accountability remains with the organisation responsible for the processing.

AI applications and agents can have too much access

When an AI tool connects to company data, its permissions matter. An application or agent with broad access may expose information beyond the task it was meant to perform. If it can take actions, rather than only produce suggestions, mistakes or manipulation may have more serious consequences.

Access should be limited to what the use case requires. That means knowing which systems, files and actions are in scope, who approved them, and who can stop or change the integration.

Prompt injection and unsafe outputs create operational risk

AI systems can be manipulated through instructions in prompts or content they process. Prompt injection may influence an application to ignore intended safeguards, disclose information or take an unintended action. Outputs can also be inaccurate or inappropriate, so they should not be treated as authoritative simply because they sound confident.

Where AI informs decisions or interacts with business processes, your organisation needs to consider how outputs are checked, what actions require human approval, and how an incident would be identified and managed.

A blanket ban is not a governance strategy

If staff have a real business need and no approved option, prohibition alone may push activity into personal accounts, browser extensions or unsanctioned applications. That reduces visibility without necessarily reducing risk.

A more sustainable approach is to understand what is already happening, make ownership clear and provide a proportionate route for safe experimentation. The NCSC’s Cyber Security Code of Practice for AI provides guidance across the AI lifecycle, from design and development through deployment and maintenance.

Build a practical control plan for AI use

Start with discovery, not a product purchase. Mapping which AI tools are in use is step one, before any product. A useful review should clarify the tools and use cases, the data involved, integrations and permissions, and who is accountable.

Then work through these priorities:

1. Create an AI inventory. Record known tools, features, applications and agents, with their business purpose, owner, data categories, provider and relevant integrations. Include experimentation and tools embedded in existing SaaS services.
2. Set clear data rules. Define what staff may use with approved tools, what needs review and what must not be entered into unapproved services. Make the rules practical enough to follow in day-to-day work.
3. Review access and identity. Apply least privilege to users, applications and agents. Consider authentication, credentials, permissions and access review as part of your wider identity and access security controls.
4. Assess providers and data paths. Understand where information goes, how it is handled and what contractual commitments apply. If AI connects to cloud services or business data, review it alongside your cloud security responsibilities.
5. Test and monitor proportionately. Decide how to validate outputs, detect unexpected behaviour and respond to incidents. Include AI-facing applications in broader exposure management, so weaknesses and ownership gaps are not left outside normal risk processes.
6. Name a business owner. IT and security can advise on controls, but the business owner must understand the purpose, acceptable outcomes and consequences of the use case. Involve privacy, compliance, procurement and legal colleagues where appropriate.

Secure experimentation without assuming zero risk

AI use cannot be made completely risk-free. The aim is to understand the exposure, reduce avoidable risks and make an informed decision about what remains.

For AI applications and agents, safeguards depend on the platform and implementation. A review may consider data access, identity, integrations, monitoring and human oversight. Do not assume that a control available in one environment applies to every tool or agent.

AI red teaming can be useful where an application warrants a closer assessment. It is a scoped exercise testing how an application behaves under selected adversarial scenarios. Scope, data handling and success criteria must be agreed before testing. It is not a guarantee that every weakness or future attack will be found.

AOS can help your organisation review AI usage and application exposure, assess security considerations for AI applications and agents, and develop practical governance and remediation recommendations. The AI security service focuses on outcomes proportionate to your use case and data. The AI Security & Governance Stack describes illustrative technology roles, not a fixed bill of materials.

Check Point is one authorised UK partner option where it fits; where another technology genuinely fits better, AOS designs and supports that too, across Sophos, Microsoft, AWS, Barracuda, TitanHQ and Acronis.

Give the board a clear, documented answer

When leadership asks whether staff use AI, what information it can access, and how the organisation knows it is safe enough for its purpose, your answer should be grounded in evidence, not assumptions.

A documented inventory, named owners, proportionate controls and a review process will not remove every risk. They do give your organisation a credible basis for decisions, safer experimentation and improvement as tools change. Every day without visibility is another day in which an unreviewed data flow or permission may go unnoticed.

Start with a free 30-minute cybersecurity review covering email security, endpoint protection, phishing exposure, ransomware protection, remote access, identity, data protection and existing controls, ending with a simple Red, Amber or Green score and prioritised fixes.