AI integration security: what your CISO needs to know

When AI can query your CRM, search your document library, and read your financial data, the security implications need careful thought. Enterprise AI platforms already include robust security features. But custom MCP integrations introduce new data access patterns that your security team should understand and approve.

What enterprise AI platforms provide natively

Enterprise AI platforms include enterprise-grade security features out of the box. SSO via SAML 2.0 and OpenID Connect. Role-based access controls. Spend controls and seat management. Compliance and monitoring APIs. Usage analytics. Managed policy settings. Data processing agreements and regional deployment options.

These features need to be configured correctly, but they exist. Your security team should review your platform's enterprise security documentation and understand what controls are available before evaluating additional layers. Many security concerns that seem like blockers have native solutions.

Where custom MCP servers add security requirements

Native platform connectors (Slack, Gmail, Jira, etc.) are built and maintained by the platform vendor or their partners. Their security model is defined and audited. Custom MCP servers that you (or your integration partner) build for internal systems introduce new security considerations because they are new code accessing your data.

• Authentication: Custom MCP servers should use OAuth 2.1 with scoped tokens tied to your identity provider. No shared service accounts. Each user's queries should execute with their own permissions.
• Access controls: The MCP server must enforce your existing access controls. If a user cannot see a record in the source system, they must not see it through the AI.
• Audit trails: Every query, every data access, every response must be logged with user identity, timestamp, and the data returned. For regulated industries, audit retention requirements apply (seven years for FCA).
• Data minimisation: MCP servers should return only the fields needed, not entire records. Do not surface fields through the AI that are not necessary for the use case.
• Infrastructure: MCP servers can be deployed in your own infrastructure (Azure UK South, AWS eu-west-2, on-premises) to ensure data residency compliance.

The AI model layer

The MCP architecture separates the model layer from the data access layer. Your data flows through your MCP servers, which you control and own. The AI model processes the query and generates a response. Enterprise AI platforms offer zero data retention options and data processing agreements that specify how data is handled.

This separation means you can control where your data is processed even when the model runs in a vendor's infrastructure. Because the data access layer is decoupled from the model and built on the open MCP standard, you are not locked to a single provider: for organisations requiring maximum control, models can also be accessed through cloud platforms such as AWS Bedrock or Google Cloud Vertex AI, providing additional deployment flexibility.

Regulatory compliance

FCA (Financial Conduct Authority)

FCA-regulated firms need to demonstrate that AI interactions with client data are logged, that access controls enforce the principle of least privilege, and that the firm maintains the ability to explain AI-assisted decisions. Your platform's compliance and monitoring APIs, combined with properly configured MCP server audit trails, support these requirements under SYSC 9.

GDPR and UK Data Protection

AI deployments that access personal data must comply with GDPR principles: purpose limitation, data minimisation, and storage limitation. Enterprise AI vendors offer data processing agreements covering AI-assisted processing. MCP servers should be configured to return only necessary fields and respect your retention policies.

NHS DSPT

NHS organisations and their suppliers must demonstrate compliance with the DSPT. For AI deployments handling patient data or clinical information, this means additional controls around data classification, access logging, incident response, and staff training. MCP servers accessing NHS systems should be deployed within N3/HSCN-connected infrastructure.

Questions to ask your deployment partner

• How are the AI platform's native security features being configured for our organisation?
• Where are custom MCP servers deployed and where does data processing occur?
• How are user permissions from source systems enforced in AI queries?
• What does the audit trail capture, how long is it retained, and in what format?
• How is the deployment tested for security vulnerabilities before going live?
• What is the blast radius if a custom MCP server is compromised?
• How does ongoing maintenance address security updates and vulnerability management?

If your deployment partner cannot answer these questions clearly and specifically, that is a signal. AI deployment security is not something to figure out afterwards. It needs to be designed into the architecture from the start.