Data security in AI-powered FP&A: Is your financial data really secure?

The question concerns every CFO and controller: Can I use artificial intelligence in financial planning & analysis (FP&A) without jeopardizing my sensitive company data? The answer is a resounding yes — but only under the right conditions. While AI solutions have the potential to revolutionize controlling processes, they also pose new challenges for data security.

The skepticism is justified: 77% of all companies using AI report security-related incidents^[1]. At the same time, 69% of German companies see data protection as a primary hurdle when it comes to AI. These figures clearly show that data security in AI controlling is not a nice-to-have, but a business-critical necessity.

Why the security question in AI-powered FP&A is justified

Financial data is one of a company's most sensitive information. They not only contain trade secrets and strategic information, but are also subject to strict regulatory requirements. The new EU AI Act, which comes into force from 2025, even classifies financial AI applications as “high-risk AI systems,” thus tightening the requirements for data security, transparency and auditability^[2].

A CFO of a construction company puts it in a nutshell:

“I want a shielded model — my data can't be on the open Internet.”

This attitude reflects what motivates many managers: The fear of losing control over their most valuable data. In contrast to other areas of the company, where data losses are annoying, they can be life-threatening in the financial sector. Liquidity forecasts, margins, customer credit ratings and strategic investment plans are information that must never fall into the wrong hands.

In addition, there is the complexity of modern AI systems. While traditional software solutions have comprehensible data flows, many AI models work as “black boxes.” Business owners often do not know where their data is being processed, who is accessing it and whether it is being used to train other models.

The most common risks and safety concerns

The concerns about data security in AI-based FP&A are manifold and well-founded. The biggest risk lies in the unwanted use of customer data to train AI models. Many global AI providers automatically use entered data to improve their algorithms. What may be acceptable for general applications is absolutely unacceptable for financial data.

Storing data in data centers outside the EU represents another critical risk. Even if providers promise data protection, they are subject to the laws of their home countries. For example, the CLOUD Act can oblige US companies to hand over data to authorities — regardless of where it is stored.

A lack of transparency further exacerbates this problem. Many AI systems do not offer auditability or traceable logging. Administrators can't verify who accessed which data and when. This lack of transparency makes it impossible to meet compliance requirements or to respond adequately to security incidents.

Finally, there is a risk of insufficient data isolation. In multi-tenant environments, vulnerabilities can result in data from different customers being mixed. For financial controllers working with highly sensitive margins and liquidity data, this is an unacceptable scenario.

Best practices for secure use of AI in controlling

Despite the risks, companies can safely use AI in FP&A if they pay attention to the right standards and practices. The most important principle is: Security by Design instead of Security as Afterthought. This means thinking about security measures from the start, rather than implementing them retrospectively.

Hosting and infrastructure

The right infrastructure forms the basis of secure data in AI-powered FP&A. All data should be processed exclusively in EU data centers that are certified in accordance with ISO 27001. This international standard for information security management systems ensures that systematic security controls are implemented and regularly audited.

In addition, companies should pay attention to SOC 2 Type II certifications, which specifically assess the security of cloud services. These certifications confirm that security controls not only exist but also work effectively.

Data protection and compliance

GDPR compliance goes without saying, but real data security goes beyond that. Privacy by design means that data protection is already integrated into the system architecture. This includes principles such as data minimization (processing only necessary data), purpose limitation (use data only for the agreed purpose) and storage limitation (only store data for as long as necessary).

Transparency is another key factor. Companies should be able to understand which data is being processed where at any time. Audit trails and detailed logging make it possible to document and review every access and change as needed.

Technical safety measures

Encryption is essential — both when transmitting and storing data. End-to-end encryption ensures that data is protected as soon as it leaves the company and remains secure even when processed in the AI system.

Data isolation prevents information from different clients from being mixed. Every customer should have a completely isolated environment in which their data is processed. Zero-trust architectures go one step further: They assume that no system is trustworthy and therefore check every access individually.

Step-by-Step: How to Evaluate the Security of AI Controlling Solutions

Selecting a secure AI solution for financial planning & analysis requires a systematic approach. This checklist helps you objectively assess the data security of various providers:

1. Verify certifications: Request evidence of ISO 27001, SOC 2 Type II, and GDPR compliance Reputable providers readily provide this information and can present current certificates.
2. Inquire about data center locations: Get a written confirmation of where your data is being processed and stored. Insist on EU locations and demand guarantees that no data transfer to third countries takes place.
3. Clarify data usage: Explicitly ask whether your data is being used to train AI models. Request a contractual assurance that your data will only be used for your own purposes.
4. Evaluate audit opportunities: Check which logging and monitoring features are available. Can you understand who accessed which data and when? Are there automatic notifications for suspicious activity?
5. Test data isolation: Learn about the technical architecture of the multi-tenant environment. How do you ensure that your data remains separate from that of other customers?
6. Check backup and recovery: Understand backup strategies and recovery processes How quickly can your data be restored in a disaster and where are the backups stored?
7. Agree on contractual securities: Obtain liability regulations, data protection obligations and termination modalities in writing. Clear notification and remedial measures should be defined in the event of security incidents.

Conclusion: Secure AI in FP&A is possible

The question “Is my financial data really secure with AI?” can be answered unequivocally with yes — if the right security measures have been implemented. The combination of EU hosting, strict certifications, data isolation, and full transparency makes it possible to take advantage of AI without sacrificing data security.

The key lies in carefully choosing the provider. Companies should not blindly rely on promises, but demand concrete proof of security measures. The EU AI Act further tightens regulatory requirements, which makes established safety standards even more important^[2].

AI in controlling offers enormous potential for automation, better forecasts and more efficient processes^[3]. Companies should not let this potential go unused out of excessive caution. Instead, it is important to make informed decisions and rely on providers who take data security in AI-powered FP&A seriously and communicate transparently.

The future of FP&A will be shaped by AI. Companies that rely on secure solutions early on gain a decisive competitive advantage without jeopardizing their most valuable asset: their data.