AI is now embedded in the core of financial services, from fraud detection and customer analytics to automated decision-making and operational efficiency. However, as banks, investment firms, and fintechs accelerate AI adoption, the security risks associated with poorly governed AI platforms are becoming one of the most material threats to operational resilience.

Artificial intelligence is rapidly transforming financial services, enabling firms to analyse data and automate complex processes at scale. Yet the same capabilities that make AI powerful can also introduce new security, privacy, compliance and operational risks when appropriate guardrails, access controls, and governance frameworks are absent or immature.

For a sector defined by trust, regulatory compliance, and rigorous risk management, unmanaged AI is more than a technology challenge. It is a strategic risk that can expose firms to cyber threats, data leakage and reputational damage.

There are five risks facing financial services firms as they adopt AI:

1. The expanding attack surface of AI platforms
2. The risk of operating without effective AI guardrails
3. The dangers of failing to perform regular access audits
4. Risks associated with inadequate vendor onboarding and offboarding processes
5. The consequences of improperly decommissioning AI platforms

1. The expanding attack surface of AI platforms

AI as a tool and function is here to stay, particularly in financial services, where it is becoming deeply embedded within core business processes. From investment research and due diligence to portfolio monitoring and investor reporting, AI offers significant opportunities to improve efficiency, accelerate decision-making, and uncover valuable insights. However, while the benefits are clear, firms must also recognise the new risks that AI introduces and the ever-expanding attack surface that accompanies its adoption.

For those unfamiliar with the term, an attack surface refers to all systems, applications, users and technologies that could be targeted by a cyber attacker. Traditionally, this included office firewalls, employee devices, servers, networks, and data centres. AI introduces an additional layer to that attack surface, creating new entry points and dependencies that organisations must identify, assess, and secure.

As AI adoption increases, so does the number of vulnerabilities. Every AI platform, data integration, API connection, and third-party provider expands the attack surface and introduces new risks that need to be considered. Without appropriate governance and security controls, organisations risk exposing sensitive business, customer, and investment data to unauthorised access, misuse or compromise.

For financial services firms, the challenge is not whether to adopt AI, but how to do so securely. As AI becomes a permanent part of business operations, innovation must be balanced with effective risk management, strong governance frameworks and operational resilience.

As adoption grows, organisations that recognise and actively manage the AI attack surface will be better positioned to realise the benefits of AI while protecting critical assets to maintain strong governance and security controls.

2. The risk of operating without effective AI guardrails

AI guardrails are a fundamental part of successful AI adoption. They help organisations balance innovation with security, ensuring AI can deliver business value while associated risks are properly understood, managed, and controlled.

But what exactly are AI guardrails?

AI guardrails are the policies, controls, and safeguards that help ensure AI is used securely, responsibly, and in accordance with business, regulatory and operational requirements. Examples include:

• Access controls: Restrict who can access and use AI platforms.
• Data protection measures: Prevent sensitive information from being exposed, shared or misused.
• Usage policies: Define how AI can and cannot be used within the organisation.
• Content filtering: Reduce the risk of harmful, inaccurate outputs.
• Monitoring and auditing: Track AI usage to support compliance and oversight requirements.
• Human oversight: Ensure important decisions are subject to human review.
• Compliance controls: Support adherence to regulatory, legal, and industry obligations.

Together, these guardrails allow organisations to adopt AI safely while managing security, privacy, compliance and operational risks. Many AI platforms provide built-in guardrails that enable organisations to help maintain control over their data, users, and AI environments, ensuring innovation is balanced with security, compliance, and risk management.

It’s important to ensure that, as AI adoption increases, guardrails should form a key part of the vendor due diligence process. Organisations should assess whether AI platforms provide the security, governance, compliance, and monitoring controls needed to support safe and responsible use of the technology.

3. The dangers of failing to perform regular access audits

Access audits form a core part of most information protection frameworks and have been a long-standing security practice within organisations. Regular access reviews help ensure that employees, contractors and third parties only have access to the systems and information required to perform their roles. This reduces the risk of unauthorised access and inadvertent data exposure. As organisations grow and systems evolve, these audits become increasingly important in maintaining the principle of least privilege, strengthening data security, and supporting regulatory compliance.

With AI adoption and the increasing use of AI agents to perform business tasks, it is crucial that organisations incorporate AI into their access review processes. Businesses should regularly assess what data AI platforms and agents can access, what actions they are permitted to perform, and what information employees can access through those platforms. This helps maintain the principle of least privilege, reduces the risk of data exposure, and ensures AI operates within established security, compliance and governance frameworks.

4. Risks associated with inadequate vendor onboarding and offboarding processes

When AI platform pilots are introduced, it is crucial that organisations have robust vendor onboarding and offboarding processes that specifically address the unique risks associated with AI.

But what are vendor onboarding and offboarding processes?

If you’re not familiar with the concept, a vendor onboarding and offboarding process refers to the structured approach organisations use to assess, approve, manage, and ultimately remove third-party vendors, systems, or services. The onboarding process ensures appropriate due diligence, security reviews, and approvals are completed before a vendor is granted access to organisational systems or data. The offboarding process ensures access is removed, data is handled appropriately, and integrations are securely decommissioned when the relationship ends.

In the context of AI, these processes become even more important. Organisations should conduct a thorough assessment of a vendor’s security controls, data handling practices, compliance certifications, and AI governance capabilities before granting any access to sensitive information. It is also critical to clearly understand what data the AI platform can access, where that data is stored, and how it is protected.

Equally important is establishing a well-defined offboarding process to ensure data access is revoked, integrations are removed, and organisational information is securely handled when the pilot or vendor relationship ends.

For financial services firms, proper decommissioning should be viewed as a critical component of the AI lifecycle. Ensuring that redundant AI platforms are securely removed helps reduce third-party risk, protect sensitive data, and maintain a strong security and governance posture.

5. Risks of not properly decommissioning AI platforms

We are experiencing a surge of AI platforms entering the market, each specialising in different use cases and offering models that excel at specific tasks. This is naturally driving organisations to pilot a wide range of AI solutions at speed and scale. However, when an AI platform is no longer required, it is important that it is properly decommissioned and removed from the business’s technology environment. Offboarding should include removing integrations, revoking access permissions, deleting or retrieving organisational data where appropriate, and ensuring no residual connections remain that could introduce security, compliance, or operational risks.

As part of the decommissioning process, the organisation should:

• Remove AI platform integrations and system connections.
• Revoke user, agent, and administrator access.
• Delete or retrieve organisational data where required.
• Cancel licences and subscriptions.
• Confirm that the platform no longer has access to company systems or information.

Balancing AI innovation with risk management

AI is delivering significant benefits across financial services, enabling organisations to improve efficiency, enhance decision-making and unlock new opportunities. However, it also introduces new security, governance, compliance and operational challenges that cannot be ignored.

As AI adoption accelerates, firms must ensure that appropriate guardrails, regular access reviews, vendor management processes, and structured platform decommissioning controls are embedded into their AI strategy from the outset. These measures help ensure that innovation is balanced with appropriate oversight and risk management.

By taking a proactive, risk-based approach to AI governance, organisations can confidently embrace AI innovation while protecting sensitive data, maintaining regulatory compliance, and strengthening long-term operational resilience.