The AI Security and Privacy Risks Enterprises Need to Know About

AI adoption is accelerating rapidly across industries, but with that surge in usage come increasing concerns over security and privacy. In the finance industry, which operates under intense regulatory scrutiny and manages vast amounts of sensitive financial data, the stakes are especially high in finance.

In this article, we’ll take a closer look at major security and privacy risks finance teams face when using AI, explore tips for mitigating these issues, and look at a solution that helps lower risk exposure from the outset.

What are the major AI security risks enterprises face?

AI can transform how financial organisations work, but it also introduces vulnerabilities that traditional risk frameworks weren’t designed to handle. By recognising these emerging challenges early, finance teams can turn AI into a tool for growth rather than a source of exposure. Let’s take a closer look at some of the main issues.

Data privacy breaches

When AI tools that are trained on public data sets ingest financial data, there is a risk that this could be exposed, accidentally shared, or even abused through malicious prompting. Public or semi-public AI models may retain or cache user inputs; if those inputs include personally identifiable information (PII) or proprietary financial data, the exposure could lead to data breach incidents.

Breaches of PII (names, addresses, customer identifiers) or financial records have large penalties under GDPR, CCPA, and other privacy laws. Additionally, “data leakage” in AI systems, where models are trained or accessed in ways that allow proprietary input data to be reconstructed or inferred, can create huge vulnerabilities.

Hallucinations and inaccurate outputs

AI models, especially large language models (LLMs), generate output by predicting what looks plausible, not by checking facts by default. This can lead to “hallucinations” — situations where the AI confidently asserts false or misleading information.

In finance, those errors can have serious consequences including incorrect forecasts, misleading management commentary, or even flawed compliance disclosures.

Bias and unfair decision-making

Even when the data AI analyses is “true,” it may carry biases. Training datasets may be skewed: overrepresent certain customer groups, underrepresent others, or reflect historical inequalities. In finance, this can show up in spend analysis, where skewed data can distort costs, and iterative forecasting, where historic inequalities impact projections.

Lack of governance and auditability

Strong governance is the backbone of safe and effective AI adoption, yet it is often overlooked. Without clear controls, finance leaders face blind spots around questions like: Which data sources powered this output? Was the model version approved? Who validated the results?

These gaps create challenges during audits and increase exposure to regulatory penalties. Black-box models that lack lineage, audit trails, or version control make it impossible to defend forecasts, reports, or ESG disclosures.

Regulatory non-compliance

Finance is one of the most heavily regulated areas in business. Tools that process personal data, provide financial advice or risk scores, or which support compliance reporting must comply with laws such as GDPR and companies must work to meet emerging frameworks like CSRD.

As AI becomes embedded in these tools, the regulatory burden intensifies: models must not only deliver accurate insights, but also demonstrate fairness, transparency, explainability, and compliance with requirements.

Why these risks matter for finance teams

Finance functions sit at the intersection of sensitive data, regulatory oversight, and strategic decision-making. This makes them uniquely vulnerable to AI security and privacy risks. Here, we take a closer look.

High-value targets for cybercrime

Financial data is among the most valuable categories of information for attackers. Transaction records, supplier contracts, payroll, and customer payment data can all be exploited for fraud or extortion. AI systems that touch this data are natural targets for cybercriminals seeking to breach, exfiltrate, or manipulate critical assets.

Heavy regulatory scrutiny

Finance teams operate under constant supervision from regulators such as the SEC, FCA, FINRA, and European supervisory authorities. Errors or lapses in governance aren’t just operational headaches — they can trigger fines, investigations, and reputational damage. If AI systems are found to mishandle data, introduce bias, or lack transparency, finance leaders will be held accountable.

Mission-critical role in business operations

Unlike experimental AI pilots in marketing or HR, finance is mission-critical. Board decisions, investor reporting, and compliance deadlines all rely on accurate, auditable information. A single AI-driven error in forecasting, ESG disclosure, or financial close can lead to material impacts on capital allocation and shareholder confidence.

Best practices for mitigating AI Security Risks

Leading finance teams are adopting the following practices to mitigate risks while adopting AI.

Centralise and govern financial data

AI can only be as secure and reliable as the data it consumes. Finance teams must consolidate fragmented systems into a single source of truth, using a platform like 5Y to standardise structures and embed governance to ensure that data is accurate, consistent, and compliant.

Use enterprise-grade AI tools with encryption & audit trails

Consumer or public AI models are rarely sufficient for financial workloads. Enterprises should select platforms that offer encryption (in transit and at rest), full audit trails, role-based access controls, and clear commitments on data residency and retention.

Keep humans in the loop for high-stakes decisions

AI can accelerate analysis and highlight anomalies, but final judgement should remain with finance professionals — especially for regulatory filings, disclosures, or board reports. Human oversight reduces the risk of over-reliance on outputs that may be incomplete or flawed.

Train staff on safe and compliant AI usage

Training programs help build a culture of responsible AI use across the function. Finance professionals must understand the risks of inputting sensitive data into AI tools, recognise the signs of biased or inaccurate outputs, and follow documented processes for AI-assisted tasks.

Use our checklist to evaluate AI tools with confidence

The 5Y AI Safety Checklist for Finance Professionals helps you evaluate AI tools, ensuring your data stays secure, compliant, and audit-ready.

How 5Y Helps Enterprises Mitigate Risks

5Y’s Business Transformation Platform is designed to help finance teams gain the benefits of AI while avoiding the pitfalls of uncontrolled adoption. The platform ensures that AI operates from a foundation of clean, governed, and connected financial data, consolidating fragmented ERP, CRM, and operational data into a single, consistent model.

Embedded governance and auditability reduce exposure to errors and ensure AI tools have reliable inputs. From metadata and lineage tracking to role-based access controls, 5Y provides the scaffolding required to meet audit and compliance standards.

Then, AI insights are surfaced through dashboards, reports, and natural-language queries, giving finance professionals the ability to interrogate results directly and apply oversight before acting.

Crucially, we train our AI tool on proprietary models rather than public data. This allows us to ensure that your data remains private, protected, and controlled.

Looking to the future

AI presents enormous opportunities for finance, but also real risks if tools are adopted without the right foundation. By centralising data, enforcing governance, and keeping humans in the loop, enterprises can use AI to accelerate reporting, improve forecasting, and reduce costs without compromising security or compliance.

Download the Finance AI and Automation Playbook to discover seven practical and safe ways to apply AI in your team.

Alongside, the playbook, our AI Safety Checklist for Finance Professionals helps you to evaluate your AI tools and keep your data secure.