Secure AI adoption for UK financial services firms

Artificial intelligence is helping financial firms improve analytics, productivity and automation. Without the right controls, however, it can also introduce risks around data security, access, compliance and governance.

Secure AI adoption in financial services means gaining these benefits without weakening existing security or regulatory obligations. This guide covers practical steps, from choosing the right use cases to protecting data, managing access and maintaining governance.

AI adoption is already underway

AI is now a regulatory focus as well as a business one. In the Bank of England's 2026 H1 Systemic Risk Survey, 32% of respondents named AI-related risks among their top five risks to the UK financial system, up from 20% in the previous survey.

Adoption was already well established before that. The Bank of England and FCA's 2024 survey on AI in UK financial services found that:

The same survey found that cybersecurity was the greatest perceived risk among firms using AI, followed by data protection, third-party dependencies and model complexity. Secure adoption therefore needs to address far more than the AI model itself.

Start with the right use cases

Begin with a clear business need rather than adopting AI because it is available. Assess the risk of each use case before deployment.

Lower-risk applications include:

• Document search and summarisation

• Data analysis and classification

• Workflow automation

• Research assistance

• Cybersecurity monitoring

  
  

Higher-risk uses need stronger controls and oversight. These include credit decisions, investment recommendations, customer profiling and compliance decisions, where AI outputs directly affect customers or regulatory outcomes.

Protect the data

AI tools can process sensitive financial, client and business information. Before deployment, firms should understand what data a tool can access and where that data is processed. Under UK GDPR, higher-risk processing may also require a data protection impact assessment.

Core data controls include:

• Data classification and encryption

• Restricted access and secure cloud configuration

• Data retention policies

• Monitoring of AI data flows

• Controls over third-party providers

  
  

Apply least-privilege access so users can reach only the data and AI tools their role requires.

Strengthen identity and access controls

AI can create new access points across applications, cloud platforms and third-party services. Strong identity controls help prevent unauthorised access to sensitive information.

Firms should review:

• Who can use AI tools

• What data and applications those users can access

• Privileged account permissions

• Access review and removal processes

• AI activity logging

  
  

Clear access controls also support accountability by recording who accessed systems and how AI was used.

Build governance around AI

Security controls alone are not enough. Firms also need clear ownership, and existing UK rules already set expectations here.

Regulation	Relevance to AI
SM&CR	Senior managers remain accountable for AI systems within their responsibilities
Consumer Duty	AI-driven decisions must support good outcomes for retail customers
UK GDPR	Covers personal data processing and solely automated decisions
PRA SS1/23	Sets model risk management principles for banks, including AI models
Operational resilience	AI supporting important business services must stay within impact tolerances

The NIST AI Risk Management Framework offers a useful structure built around four functions, Govern, Map, Measure and Manage, with continuous risk management across the AI lifecycle.

A practical governance structure could include:

Area	What to establish
Ownership	Who is accountable for each AI system
Use cases	Documented purpose and expected outcomes
Risk	Assessment of security, privacy and operational risks
Access	Who can use the system and its data
Testing	Checks for accuracy, reliability and security
Monitoring	Ongoing review of performance and emerging risks
Documentation	Records for audit and oversight

This creates a clear line between experimentation and controlled production use.

Manage third-party AI risk

With one-third of AI use cases involving third parties, external platforms and cloud providers are a significant source of risk. The UK's critical third parties regime also reflects growing regulatory attention on firms' reliance on outside technology providers.

Before adopting a third-party AI service, assess:

• Where data is stored and processed

• Security and access controls

• Data retention and deletion

• The provider's own dependencies

• Incident response and availability

• Contractual and regulatory requirements

• Business continuity arrangements

  
  

These checks should sit within the firm's wider third-party risk management process.

Keep humans in the loop

AI should support decisions, not replace accountability. This matters most for customer, investment, compliance and financial risk decisions.

Oversight controls include:

• Reviewing AI-generated outputs

• Setting approval thresholds

• Escalating unusual results

• Testing performance regularly

• Keeping decision records

  
  

The 2024 Bank of England and FCA survey found that 55% of AI use cases involved some automated decision-making, while only 2% were fully autonomous. Human oversight remains central to responsible adoption.

Make security part of the architecture

AI should not sit outside a firm's existing technology controls. It should be built into the wider security and cloud architecture, with audit trails, identity governance and compliance controls applied from the start.

Alternit One's AI, Cybersecurity, Cloud Infrastructure and Compliance & Due Diligence services help firms integrate AI securely within their cloud environments and meet regulatory expectations.

Adopt AI with confidence

AI can deliver real gains in productivity, analytics and efficiency when it rests on secure foundations. Firms that combine data protection, access management, third-party oversight, human review and clear governance can scale AI while meeting regulatory expectations.

Planning your next AI initiative? Alternit One helps financial firms build AI, cybersecurity, cloud and compliance strategies that stand up to regulatory scrutiny. Talk to Alternit One