Enterprise AI now reaches far beyond public chat tools. Employees use copilots in everyday applications. Teams build internal assistants over company documents. Developers connect models to APIs, databases and business workflows. Some systems can also take actions on a user's behalf.

Each use creates a different security question. What data can the system access? Which identity does it use? Can it call another service? Can a user or an external source change its behaviour? What happens when it produces an unsafe result?

The concern is already visible among security leaders. Proofpoint's 2026 Voice of the CISO study found that 78% of global CISOs viewed generative AI as a security risk. The figure rose 18 percentage points year over year. The research surveyed 1,600 CISOs across 16 countries. Read Proofpoint's 2026 findings.

Enterprise AI security needs to cover the full system. That includes models, data, applications, users, integrations and the actions an AI agent can take.

Start by identifying what counts as enterprise AI

A useful AI inventory should include more than approved projects. It should record the systems employees and business units already use.

The inventory may include:

·       Public or enterprise versions of generative AI tools.

·       Copilots embedded in productivity, CRM, HR and development applications.

·       Internal chatbots that search policies, contracts or technical documents.

·       Retrieval-augmented generation systems that use enterprise data sources.

·       AI features built into customer-facing applications.

·       Model APIs connected to internal software.

·       Agents that can create tickets, query systems or trigger business actions.

·       Third-party AI services and plugins used by suppliers.

These systems do not share the same risk profile. A tool that answers questions from public information has different access needs from an assistant that searches customer records. An agent that can update a production system requires more controls than a tool that only drafts text.

The inventory should capture the business owner, technical owner, model or provider, data sources, user groups, integrations and actions. It should also record whether the service stores prompts or uses submitted data for model improvement.

Six security risks to assess

1. Sensitive information exposure

Employees may paste contracts, customer details, source code or internal reports into an AI tool. A connected assistant may retrieve documents that the user should not be able to access. An output may also reveal sensitive information to another user.

This is a data access problem as much as an AI problem. The model should not receive information that the user is not authorised to see. Retrieval systems must preserve the source permissions when they search and return documents.

2. Prompt injection and untrusted content

A prompt injection attack uses instructions in user input or external content to influence an AI system. The instructions may ask the model to ignore its rules, expose information or take an unwanted action.

The risk can appear in uploaded documents, web pages, emails or retrieved content. Security teams should not assume that a system prompt alone can prevent this behaviour. Input handling, output controls, data permissions and action restrictions all matter.

The OWASP GenAI Security Project lists prompt injection, sensitive information disclosure, supply-chain weaknesses and excessive agency among its 2025 risks for LLM applications.

3. Excessive permissions and agent actions

An AI assistant may be connected to calendars, email, ticketing systems, databases or business applications. If it receives broad permissions, a mistake or manipulated instruction can affect more than the conversation.

Grant only the access required for the use case. Separate read and write permissions. Require human approval for high-impact actions. Log the tool calls and the identity under which each action runs.

4. Insecure integrations and supply chains

Enterprise AI may depend on model providers, data connectors, vector databases, plugins and external APIs. Each component adds a possible failure point. A weak integration can expose credentials or give an application more access than intended.

Review the security of connectors and dependencies. Store API keys in approved secret-management systems. Define how suppliers handle data, updates, vulnerabilities and incidents.

5. Unreliable or unsafe outputs

AI systems can produce incorrect answers, inaccurate summaries or unsafe recommendations. In a business workflow, these outputs may influence customer communication, financial decisions or technical changes.

The level of review should match the impact. A low-risk draft may need a quick human check. A decision affecting a customer, employee or production system needs stronger validation and an accountable owner.

6. Limited monitoring and incident response

Traditional security monitoring may not record the full AI interaction. The organisation may know that a user accessed an application but not which data the assistant retrieved or which tools it called.

Define what needs to be logged. This may include user identity, model version, data sources, prompts, tool calls, approval events and outputs. Set retention and privacy rules before collecting sensitive logs.

A practical framework for enterprise AI security

A security programme should follow the AI system from discovery to daily operation. The steps below provide a starting point.

Step 1: Create an inventory and assign ownership

Identify approved and unapproved AI tools. Record their business purpose, users, model provider, data sources and integrations. Assign a business owner and technical owner to each system.

Classify use cases by risk. Consider the sensitivity of data, the number of users, the external exposure and the actions the system can perform. Give higher-risk systems additional review before release.

Step 2: Set clear rules for data use

Define which data employees can enter into approved AI tools. Separate public, internal, confidential and restricted information. Set rules for customer data, employee records, credentials, source code and regulated information.

For internal assistants, enforce permissions at the source. A document search tool should return only material the user can already access. Do not rely on the model to decide whether a user is authorised.

Arche's Data Security practice describes data discovery and classification, encryption, access controls, backup and data leakage prevention. These controls support the data layer of an enterprise AI programme.

Step 3: Protect identities and permissions

Give AI applications their own managed identities. Do not share administrator credentials with an agent. Use least privilege for users, service accounts and integrations.

Review the access granted to every connector. An assistant that only needs to read a knowledge base should not receive permission to modify records. A workflow that sends messages should have clear approval rules and destination limits.

Step 4: Secure the model and its integrations

Choose approved providers and review their security terms. Confirm how prompts, outputs and connected data are stored. Check whether data is used for model training. Review retention, data residency, deletion and incident notification terms.

Use secure APIs and managed secrets. Validate inputs and outputs where appropriate. Apply rate limits and usage controls. Separate development, test and production environments.

Step 5: Test before release

Test the system with realistic data and user roles. Include attempts to access restricted documents, manipulate instructions and trigger unauthorised actions. Check how the assistant behaves when a source is unavailable or an answer is uncertain.

For systems that use retrieval, test whether access permissions remain in place across every data source. For agentic workflows, test each connected tool and approval step. Record the results and fix high-risk gaps before production use.

Step 6: Monitor and improve

Monitor unusual access, unexpected connector activity, repeated failed requests and attempts to reach restricted data. Track model and application changes. Reassess security when a new data source, tool or user group is added.

Prepare incident procedures for data exposure, compromised credentials, unsafe actions and provider outages. Include the people who own the AI application and the business process it supports.

The NIST Generative AI Profile provides a risk-management reference for organisations designing, deploying and using generative AI systems. It can support governance discussions alongside an organisation's existing security framework.

Questions to ask an AI platform vendor

A product may offer strong model performance but weak enterprise controls. Include security and procurement teams in the evaluation.

Ask the vendor:

1.          Is customer data used to train or improve shared models?

2.          Can data retention and deletion rules be configured?

3.          Where are prompts, outputs and retrieved documents processed and stored?

4.          Does the platform support single sign-on and role-based access?

5.          Can administrators control which users access each model and feature?

6.          How are connectors authorised and monitored?

7.          Can the platform show which documents and tools contributed to an answer?

8.          Are agent actions logged with the user, service identity and approval record?

9.          How are model updates and changes to connected services communicated?

10. What happens during a security incident or service exit?

Ask for evidence, not only policy statements. Review security certifications, independent assessments, incident processes and technical documentation. Test the controls in a proof of concept.

Who should own enterprise AI security?

AI security cannot sit with one team alone. Business units understand the use case. IT teams manage applications and infrastructure. Data owners define who may use information. Security teams assess threats and controls. Legal, privacy and procurement teams review obligations and providers.

A practical ownership model assigns one business sponsor and one technical owner to each AI service. Security approves the control requirements. Data owners approve sensitive data use. Application teams maintain integrations and access rules. Internal audit or risk teams review evidence based on the organisation's governance model.

The organisation should also define a simple approval path for new AI use cases. That makes it easier for employees to use approved tools without creating hidden systems outside security review.

Where existing security services fit

Enterprise AI introduces new components, but many of its risks connect to familiar security areas. Data classification, identity controls, application security, infrastructure protection and monitoring remain relevant.

Arche's Cybersecurity practice lists advisory, implementation and managed security services. Its Managed SOC service describes 24/7 monitoring, analysis and response across IT infrastructure. These capabilities can support the wider monitoring and response requirements around enterprise AI.

The healthcare cybersecurity case study also illustrates the importance of access control and protection for sensitive records. It is a useful reference for organisations handling personal or regulated data.

The precise scope of an AI security engagement should be agreed during discovery. It should identify the systems, models, data sources and actions that need assessment.

Frequently asked questions

What is enterprise AI security?

It is the set of policies and technical controls used to protect AI applications, models, data, users and integrations. It also covers the actions an AI system can perform.

What is the biggest risk when employees use generative AI?

The risk depends on the tool and the data involved. Common concerns include sensitive information exposure, weak access controls and unapproved services. Start by identifying which tools employees use and what information they can access.

Is a secure cloud platform enough to protect an AI application?

No. Cloud security is one layer. The organisation must also control data permissions, model access, integrations, prompts, outputs and any actions the application can perform.

How should an enterprise secure an internal AI assistant?

Start with an approved use case and a data inventory. Enforce source permissions, least-privilege access and secure integrations. Test for prompt injection and unauthorised retrieval before release.

Make AI adoption accountable from the start

Enterprise AI security works best when it is part of system design. Identify the tools and data first. Set clear access rules. Test the system before release and monitor it in daily use.

Explore Arche's cybersecurity capabilities and data security services, or contact the team to discuss your security requirements.