Navigating the Risks of AI in Professional Services

Article

August 21, 2025

Artificial intelligence (AI) and generative AI tools continue to become more embedded in the operations of professional services firms. In fact, a 2024 Wharton of over 800 senior business leaders found 72% use generative AI weekly in the workplace.

AI technologies’ transformative potential is undeniable, offering efficiency, scalability and innovation. However, they also introduce a new set of cybersecurity risks that firms must proactively manage to protect their clients, reputation and long-term viability.

Benefits of Using AI in Professional Services

AI can transform the way professional services firms work by streamlining operations, enhancing decision-making and improving client engagement. For instance, natural language processing tools, generative AI and machine learning can allow professionals to automate routine tasks, such as document review and data analysis, freeing up more time to focus on strategic, high-value work.

Predictive analytics and data-driven insights support better risk assessments and financial planning, while AI-powered platforms can improve compliance through automatic data classification and encryption. These efficiencies not only boost productivity but also elevate the client experience through faster, more personalized service delivery.

However, to harness the potential of AI responsibly, firms need to consider the associated risks and implement robust governance frameworks.

Identifying Professional Services AI Risks

AI misuse can affect data privacy, intellectual property, regulatory compliance and client trust, and could disrupt service delivery and client relationships if not addressed thoughtfully. Understanding these challenges is the first step toward building responsible, resilient AI strategies that align with the values and expectations of both clients and professionals.

Data Privacy and Confidentiality

AI systems often rely on large datasets, some of which may include sensitive client information. If not properly governed, this data can be exposed through improper access controls, prompt injections, model vulnerabilities, third-party integrations or cloud storage vulnerabilities.

Information Management

Generative AI can create content that resembles existing copyrighted material or raise questions about ownership of outputs. This is particularly relevant in industries where original work is a core deliverable.

AI models can reflect and amplify biases present in their training data, potentially leading to discriminatory outcomes or flawed recommendations. In fields where precision is critical, such as legal analysis or financial modeling, errors can have serious consequences.

Regulatory and Compliance Complexity

AI adoption is outpacing regulatory frameworks in many jurisdictions. While there are some frameworks to help guide mitigation like ISO/IEC 42001, NIST AI RMF and OWASP Top 10, or large language models (LLMs) and MIT AI Risk repository, firms must stay ahead of evolving laws and regulatory requirements to avoid compliance pitfalls.

Maintaining Client Trust and Transparency

Clients may be wary of AI involvement in services they expect to be human-led. They may be concerned about their private information being used in AI. Lack of transparency can erode trust and damage relationships.

Mitigating Generative AI Risks in Professional Services

While crafting a proactive generative AI risk mitigation strategy is paramount for all businesses, professional services firms can take actions to protect their firms and their clients. By thoughtfully addressing the associated risks, firms can harness these technologies to enhance service quality, drive innovation and build stronger client relationships. The key lies in combining technological advancement with responsible governance and a human-centered approach.

Adopt strict data governance policies. Use AI tools that support data anonymization and operate within secure, compliant environments. Limit access to sensitive data and conduct regular audits.
Establish clear internal guidelines on the use of generative tools. Review contracts to define ownership of AI-generated content and consult legal counsel when integrating AI into client-facing work.
Implement human-in-the-loop review processes. Use AI as a support tool rather than a decision-maker, and validate outputs against trusted sources before client delivery.
Use diverse and representative datasets. Regularly test models for bias and involve cross-functional teams in model evaluation to bring multiple perspectives to the table.
Monitor regulatory developments closely. Collaborate with legal and compliance teams to align AI use with current and anticipated requirements.
Communicate openly about how AI is used in service delivery. Highlight the benefits while reinforcing the role of human oversight.

Your Guide Forward

Navigating the rapid evolution of AI and AI governance may seem daunting. Cherry Bekaert’s Cybersecurity practice can help your firm create and implement an AI risk management strategy, including risk assessment, data and security management, and creating evaluation benchmarks.

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