AI Security for Finance: Risks, Controls, And Governance for Financial Institutions

Key Takeaways

• AI security for finance requires a specialized approach that takes into account the unique risks of the technology and how it interacts with financial workflows.
• Financial institutions face a range of AI security threats, including prompt injection attacks, data leakage, shadow AI, third-party model vulnerabilities, and insecure AI integrations.
• Traditional cybersecurity controls alone are not sufficient; financial institutions need dedicated AI governance controls covering data security, AI monitoring, and model testing.
• CISOs developing AI security for finance strategies should prioritize high-risk AI use cases, monitor for shadow AI, assess third-party dependencies, and implement layered security controls.

Defining AI Security in the Financial Industry

AI security for finance refers to protecting the AI models, applications, and agents used within a financial institution. While AI offers a range of advantages for financial institutions, including enhanced operational efficiency and the introduction of new services, it also creates new opportunities for attackers. Malicious actors can exploit AI systems, using them as entry points for their attacks, exposing data shared with the model, influencing model outputs, and manipulating the decisions that autonomous agents make.

AI systems in finance also pose unique challenges compared to those in lower-risk, less-regulated industries. Targeting AI in the financial industry can be extremely profitable, with the opportunity for fraud and gaining access to substantial sums of money. AI systems increasingly interact with sensitive data, influence financial decisions, support critical business processes, and shape customer experiences. Therefore, AI security failures in finance also cause major compliance issues and reputational damage.

The nature of the financial sector makes it a constant target for cyberattacks. With the widespread integration of AI technology in finance, cybercriminals now have new systems to exploit for their attacks, systems that have increasing access and autonomy, and operate at machine speed. These risks make dedicated AI security controls for financial institutions a necessity, not an option.

Organizations require robust and comprehensive safeguards that minimize financial services AI security risks. These safeguards must extend beyond traditional cybersecurity practices to address the specific risks created by AI interactions in FIs. AI security for finance must block specific threats that aim to manipulate model performance to expose data or affect decision-making. While also accounting for AI’s finance-specific use cases, for example:

• Banking: Securing AI systems that handle credit scoring, fraud detection, virtual assistants, and customer onboarding.
• Insurance: Protecting predictive models used for underwriting, claims automation, and AI-assisted risk assessments from manipulation or data exposure.
• Payments: Safeguarding AI used in transaction monitoring, anti-money-laundering (AML) systems, and automated payment processing against fraud and unauthorized access.

The Unique AI Security Risks in Finance

Every industry is facing security challenges as AI becomes embedded in business operations. However, FIs operate under unique pressures that make AI security in banking and broader financial services particularly challenging.

The stakes are always higher when it comes to money, and FIs are built on trust and the ability to make customers comfortable having them manage their funds. This trust requires taking security and data protection seriously, establishing a strong industry reputation, and continually demonstrating compliance.

Successful cyberattacks in the financial sector break this trust, and the resulting consequences extend far beyond technical disruption. In many industries, compromising an AI system can lead to lower productivity, poor customer experiences, or inaccurate recommendations. In financial services, the same type of failure could result in fraudulent transactions, regulatory investigations, customer harm, financial losses, and significant reputational damage.

This is why financial services-related AI security risks require a more rigorous approach than standard security programs in less regulated sectors. Security teams in FIs are not only protecting digital assets – they are protecting large sums of money, the credibility of financial decisions, and the institution’s reputation. Introducing AI systems into this high-stakes environment creates major risks that typical cybersecurity and operational risk frameworks are simply not equipped to handle.

Instead, financial institutions need a specialized approach that addresses the unique intersection of AI, high-risk workflows, and regulatory obligations. This regulatory scrutiny is also only increasing as governments around the world catch up with rapid AI deployment, determining what is expected from organizations utilizing the technology.

Top AI Security Threats Impacting Financial Services

AI security for finance must address new attack surfaces created by the technology. Cybercriminals can now develop attack vectors targeting different AI stages from their training data and internal operations to outputs, decision-making, and third-party ecosystems.

Listed below are the top AI security threats that Chief Information Security Officers (CISOs) need to be aware of.

Prompt Injection Attacks

Prompt injection occurs when an attacker manipulates the inputs to an AI system to influence its behavior and bypass internal safeguards. As generative AI security in finance becomes more important, prompt injection is emerging as one of the most significant AI-specific attack vectors. Successfully targeting FIs via prompt injection enables the:

• Exposure of confidential customer or business data.
• Manipulation of recommendations from trading models, fraud detection systems, risk analysis, and other AI systems.
• Disruption of automated business processes.

Data Leakage

Manipulating a model to leak sensitive data in its output. Employees often share sensitive data with AI models in an unsafe manner, without following proper Data Loss Prevention (DLP) practices. Whether it is customer records, financial reports, or transaction histories, including this information in prompts can lead the model to return sensitive data during later interactions. This causes:

• Regulatory violations involving customer data.
• Loss of intellectual property.
• Privacy breaches, reputational damage, and loss of customer trust.

Shadow AI And Unapproved AI Usage

Another way employees can create unnecessary AI security risks is by using unapproved services or shadow AI. Using AI without the knowledge of security teams means there is no oversight and no formal governance structure in place to protect any data shared or monitor outputs. Shadow AI leads to:

• Reduced visibility into organizational AI usage.
• Inconsistent or absent security controls.
• Uncontrolled data exposure.
• Regulatory compliance gaps.

Third-Party Model and AI Supply Chain Risk

Many financial institutions rely on external AI providers, foundation models, and AI-enabled software platforms. While these services accelerate deployment, they also expand attack surfaces beyond organizational control. Risks include vendor security incidents, changes to model behavior, and a lack of transparency into training data or security practices. Attacks targeting third-party models cause:

• Service disruptions.
• Increased operational risk.
• Compliance and audit challenges.

To counter these, AI risk management for banks requires extensive vendor evaluation.

Insecure APIs And AI Integrations

Modern AI systems depend heavily on APIs to access data, interact with applications, and automate business processes. Poorly secured APIs can expose sensitive information, create unauthorized access paths, or allow attackers to manipulate AI-driven workflows. Common examples include APIs with excessive permissions, weak authentication controls, or insufficient monitoring of AI interactions. These vulnerabilities can cause:

• Data breaches.
• Disruption of critical services.
• Expanded exposure to external threats.

Essential AI Security Controls for Finance

After identifying and assessing the top AI threats for FIs, the next step is to map them to practical security controls. These controls help CISOs and security teams mitigate risk, maintain compliance, and preserve trust while enabling AI adoption. An effective AI security for finance strategy integrates these controls into its broader security posture to simplify operations, rather than treating AI as a standalone initiative. Essential AI security controls for financial institutions include:

AI Governance

AI governance provides a framework for responsible AI use, defining specific policies, guidelines, regulatory alignment, and oversight structures. AI governance in financial services is strategic and policy-focused; it defines the rules enforced through specific protections and controls. The goal of a governance framework is to ensure that AI systems are protected against misuse, that models operate as expected, and that sensitive financial data remains secure. Key actions to take when defining an AI governance framework include:

• Define acceptable AI use policies
• Maintain an approved AI model inventory
• Establishing roles and responsibilities among key parties involved

When implemented successfully, AI governance policies help mitigate the risks posed by AI use, particularly third-party model risks, shadow AI, and noncompliance with regulatory requirements.

Data Protection and Privacy

Data protection ensures that sensitive financial information is safeguarded across all AI workflows. It focuses on preventing unauthorized exposure of regulated data, supporting compliance, and maintaining customer trust. To achieve this, FIs should:

• Apply classification and labeling to AI inputs and outputs, identifying sensitive information.
• Mask or anonymize before sharing it with AI models.
• Limit what employees can share with AI tools.

These practices help mitigate risks such as data leakage, prompt injection exposure, and regulatory compliance failures, providing a secure foundation for AI-driven financial operations.

Access Controls and Identity Management

Access controls limit AI usage to only authorized individuals, ensuring accountability across AI workflows. Proper identity management reduces internal misuse and protects sensitive financial processes. Within AI security for financial services, these controls also provide a structured way to prevent the unauthorized manipulation of AI systems. Robust AI access controls should:

• Require strong authentication methods, including multi-factor authentication.
• Track and audit user activity to detect unusual or high-risk behaviors.
• Apply role-based access control (RBAC) and least privilege principles for AI platforms and models.

When consistently applied, access controls mitigate identity abuse, unauthorized AI actions, and internal misuse.

Third-Party Risk Management

Third-party risk management addresses the vulnerabilities introduced when financial institutions rely on external AI providers. The goal is to ensure vendor relationships are transparent, secure, and aligned with regulatory expectations. This includes:

• Vendor risk assessments focused on AI security and operational resilience.
• Monitoring vendor updates and changes to model behavior.
• Including contractual obligations for compliance and audit support.

AI Monitoring

Continuous monitoring ensures that AI systems operate as intended, that outputs do not contain sensitive data, and that malicious users are blocked. Monitoring AI usage effectively requires real-time, comprehensive visibility across the entire organization as well as runtime protections that identify unusual model behavior. AI monitoring tools should:

• Implement real-time monitoring of AI outputs, prompts, and user interactions.
• Detect anomalous patterns that may indicate prompt injection or AI-enabled fraud.
• Maintain logs to support audits, regulatory reporting, and post-incident investigations.

Proactive monitoring reduces the risk of prompt injection attacks, data exfiltration, shadow AI misuse, and fraud, enhancing resilience across financial AI workflows.

Consistent Model Testing

Testing and validation ensure that AI models perform reliably, resist manipulation, and deliver consistent, compliant results. This practice supports AI security for finance programs by mitigating model errors, protecting against AI poisoning, and reinforcing trust in AI decisions. Integrating AI testing requires:

• Conducting regular adversarial testing and red-team exercises on AI systems.
• Validating model performance under diverse scenarios.
• Testing APIs and data pipelines for vulnerabilities.

These AI security controls for financial institutions mitigate risks related to model manipulation, inaccurate decisions, and API exposure.

Prioritizing AI Security for Finance

For CISOs and security leaders, addressing AI security for finance requires a structured, action-oriented approach. The following sequence provides a practical roadmap for prioritizing AI security and risk management across financial services:

1. Identify High-Risk AI Use Cases: Catalog all AI systems and workflows, including both sanctioned and shadow deployments. Then evaluate the potential business impact of failures in each use case, considering regulatory exposure, financial losses, and customer trust.
2. Review Third-Party AI Dependencies: Map all AI vendors, foundation models, and external integrations. Conduct risk assessments and prioritize oversight for vendors supporting high-impact or customer-facing systems.
3. Monitor for Shadow AI: Implement monitoring tools and internal reporting mechanisms to detect unapproved AI usage. Also, promote approved, secure AI alternatives to employees to reduce the likelihood of them using shadow tools.
4. Establish Cross-Functional Governance: Create an AI governance board with representation from security, legal, and business teams. Define responsibilities and regularly review policies, controls, and risk assessments to ensure alignment with regulatory expectations and business priorities.
5. Implement Layered Security Controls: Apply the core AI security controls outlined previously, prioritizing the high-risk AI use cases established earlier. Finally, stay up to date on the latest threats targeting AI systems.

Using Check Point to Safely Adopt AI Across Financial Services

AI adoption in financial services does not have to come at the expense of security or compliance. By systematically assessing exposure and establishing a strong AI governance framework, you can safely make the most of AI technology in financial services.

To simplify this process, Check Point offers a comprehensive suite of AI security solutions that maximize protection for every AI interaction from a single platform. Check Point’s AI security solutions ensure sensitive data is handled responsibly, models behave as expected, and outputs are solely governed by AI analysis, not malicious interference.

Learn more about how to upgrade AI security for finance by scheduling a demo with Check Point today. Alternatively, learn more about the current AI threat landscape with the latest Check Point AI Security Report.