In short
The main AI security risks for businesses are shadow AI, sensitive data in prompts and uploads, oversharing through approved assistants, prompt injection, risky browser extensions, coding assistants handling source code, AI agents and MCP connections acting with real permissions, and harmful or wrong outputs. Most are data protection risks that discovery, policy and inline controls can reduce.
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Why AI changes the risk picture
Most security controls were built for a world where data moved through email, file shares and known applications. AI changes three things at once:
- Free text is the new file transfer. A prompt can contain a whole contract, a customer list or a block of source code, typed or pasted in seconds. Check Point notes that legacy data loss prevention fails to understand conversational prompts.
- New tools arrive faster than approval processes. Staff can sign up to a new AI service, install an extension or connect an agent in minutes, with no procurement step.
- AI can act, not just answer. Agents and coding assistants can read files, run commands and call other systems with the permissions of the person who set them up.
None of this means AI should be avoided. It means you need to know what is in use, decide what is acceptable, and put controls where the data actually moves. We do not quote industry statistics here; the useful number is the one from your own organisation, which is what a discovery exercise gives you.
The AI security risks, with examples
1. Shadow AI AI tools, extensions, desktop apps and agents used without approval, often with personal accounts. The organisation does not know which providers hold its data, what their terms say about training and retention, or where the data is stored. Example: a team uses a free AI transcription tool for client calls. More in what is shadow AI.
2. Sensitive data in prompts and uploads Personal data, client files, credentials, financial figures and source code pasted into a chat or uploaded as a document for summarising. Once sent, you depend on the provider's terms for what happens next. Example: someone uploads a spreadsheet of customer complaints to get a summary for a board pack. See AI data leakage and ChatGPT security risks.
3. Oversharing in approved assistants An approved assistant that reads your files can only surface what a user already has permission to see, but many organisations have years of loose sharing. Example: a user asks an assistant about salaries and it finds a spreadsheet that was shared too widely. See Microsoft Copilot security.
4. Prompt injection Hidden instructions in a web page, document or email that an AI tool reads and follows. The OWASP Top 10 for LLM Applications 2025 lists prompt injection first (LLM01:2025), and the NCSC has written that large language models do not enforce a security boundary between instructions and data in a prompt. Example: an assistant asked to summarise a web page follows text on that page telling it to include a link to a malicious site. See prompt injection.
5. Risky AI browser extensions AI extensions that read page content, form fields or the clipboard to provide their features. Some are well run; others are unknown quantities with broad permissions.
6. Coding assistants and source code Developers paste code, configuration files and sometimes secrets into assistants, or let an assistant read a whole repository. See AI coding assistant security.
7. Agents and MCP acting with real permissions AI agents connect to email, files, ticketing systems and code repositories, often through Model Context Protocol (MCP), an open-source standard for connecting AI applications to external systems. An unvetted MCP server or an over-permissioned agent can read, change or send data. See MCP security.
8. Harmful or wrong outputs AI can produce inaccurate, biased or unsafe content that is then sent to a client or used in a decision. This is mainly a policy and review risk rather than a technical one.
Each risk mapped to a control
How Check Point Workforce AI Security addresses each risk, based on Check Point's own product material. Where a risk is mainly solved elsewhere, we say so.
| Risk | Workforce AI Security control | Other controls that matter |
|---|---|---|
| Shadow AI | Discovery of sanctioned and shadow AI apps, tools and MCPs; AI application catalogue with risk scoring; block access to unauthorised AI apps | An approved alternative and a clear AI acceptable use policy |
| Sensitive data in prompts and uploads | Contextual AI-powered DLP; inline redaction with labelled placeholders; file and image redaction with OCR; copy and paste restrictions | Data classification and staff training |
| Oversharing in approved assistants | Per-app policies and DLP on what is sent to the assistant | Fixing file permissions and sensitivity labels in the platform itself |
| Prompt injection | Runtime controls that block risky agentic actions; AI-powered guardrails | Least privilege for agents; human approval for sensitive actions |
| Risky AI browser extensions | Discovery of browser extensions in use, with policy per app | Browser management to control which extensions can be installed |
| Coding assistants and source code | Enterprise tier coverage of code assistants such as GitHub Copilot and Cursor; detection of source code and credentials in prompts | Secret scanning and repository access controls |
| Agents and MCP | Discovery of agents and MCPs; governance of agent actions; rules against risky connections between AI tools and corporate resources | An approval process for new agents and connectors |
| Harmful or wrong outputs | Visibility of which tools are used for which tasks | Policy that requires human review of AI output |
Coverage of desktop AI apps, coding assistants and agents needs the Enterprise tier; Essentials covers AI used through web applications. See how Workforce AI Security works for the detail.
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AI security examples by department
Risks look different across a business, which is why discovery comes before policy:
- Sales and marketing: customer lists and pipeline data pasted in to draft emails; AI features switched on inside marketing SaaS tools without review
- Finance: unpublished results, payroll or bank details in prompts used to build reports
- HR: CVs, grievance notes and health information uploaded for summarising, which brings special category data into play under UK GDPR
- Legal and compliance: contracts and privileged advice uploaded to consumer tools
- IT and development: source code, configuration files, API keys and logs pasted into assistants; agents connected to repositories and cloud consoles
- Leadership: board papers summarised in a personal AI account on a phone
These are illustrative patterns, not incidents involving any particular organisation.
How to reduce AI security risks
A practical order of work that does not stop people using AI:
- Find out what is in use. Run discovery across browsers and, where relevant, desktops, so you see shadow AI, extensions and agents as well as approved tools.
- Classify the data that matters. Decide which data types must not reach unapproved AI tools: personal data, client data, credentials, source code and financial information are the usual start.
- Approve a business-grade tool. Give people a sanctioned option with suitable data terms so they have less reason to use personal accounts.
- Write the rules down. An AI acceptable use policy for staff and an AI security policy for the security team.
- Enforce inline. Use redaction, warnings, copy and paste limits and blocking rather than relying on people remembering the policy. Our guide on how to block AI tools explains when to block and when to govern.
- Assess and review. Record each tool in an AI risk assessment, run a DPIA where needed, and review as tools and use change.
Is AI security risk review the right fit for you?
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How we help
Forever Systems can run a discovery pilot with Workforce AI Security so you see your real AI use, then help you prioritise the risks that matter, set policies and roll out controls in stages. Where a risk is better handled elsewhere, we say so, for example by fixing file permissions before an assistant goes live.
To talk through the AI security risks in your organisation, contact us.
How Forever Systems helps you use AI safely
We are an independent UK IT and cyber security provider. We supply Check Point Workforce AI Security and help organisations put sensible, workable rules around AI at work.
- See what is really in use: we can start with a discovery pilot, so you know which AI tools, browser extensions, coding assistants and agents your people use before anyone writes rules
- Policy you can enforce: we help you decide what to approve, restrict or block, and turn your AI acceptable use policy into Workforce AI Security policies
- The right tier: Essentials for browser-based AI, Enterprise when desktop apps, developer code assistants and agents are in the picture, confirmed at quote
- Staged roll-out: from monitoring to warnings, redaction and blocking at a pace your people can absorb, with reviews along the way
- Around the tool: Microsoft 365 permissions ahead of Copilot, device management for the extension and agent, and safe use of AI training for staff
We work with businesses, schools, charities and public sector organisations. Our guides are general information, not legal advice.
Frequently asked questions
What are the security risks of AI?
For organisations using AI tools, the main risks are shadow AI, sensitive data in prompts and uploads, oversharing through approved assistants, prompt injection, risky browser extensions, coding assistants handling source code and secrets, agents and MCP connections acting with real permissions, and inaccurate or harmful outputs.
What is the biggest AI security risk for a small business?
Usually sensitive data going into AI tools that nobody has approved, often through personal accounts. It needs no attacker, it happens through ordinary work, and it is hard to undo. Discovery, an approved tool and a short policy address most of it.
Is prompt injection a real risk for businesses?
Yes, especially where AI tools browse the web, read email or documents, or act as agents. OWASP lists prompt injection first in its Top 10 for LLM Applications 2025, and the NCSC has explained that language models do not separate instructions from data in a prompt.
Can AI security risks be managed without banning AI?
Yes. Most organisations approve a business-grade tool, restrict others with redaction, warnings or copy and paste limits, and block only the highest-risk tools. That keeps the productivity benefit while keeping sensitive data in approved places.
How does Workforce AI Security reduce AI risk?
It discovers the AI tools, extensions, agents and MCPs in use, applies per-app policies, and uses contextual DLP to redact or block sensitive data in prompts, uploads, files and images. The Enterprise tier extends this to desktop AI apps, coding assistants and agents.
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