
AI Compliance: Why Businesses Should Act Now
Adapting to new technologies always brings growing pains — especially when it comes to compliance. Artificial intelligence (AI) is transforming how companies operate, but it also introduces complex governance and risk management challenges. As organizations adopt AI tools, clients, regulators, and stakeholders increasingly want assurance that the technology is being used responsibly and transparently.
Traditional reporting frameworks such as SOC 2 provide a foundation for demonstrating internal controls, but they were not designed to fully address AI-related risks. That’s where emerging standards like ISO 42001 come in — offering a structured approach to managing AI systems ethically and safely. However, because ISO 42001 is still relatively new, many businesses have not yet reached the level of maturity needed for full certification. This can create uncertainty about how to communicate the company’s progress in responsible AI implementation.
Even so, organizations can still build trust by documenting their existing safeguards and showing a proactive approach toward AI governance. Demonstrating accountability, even before formal certification, reflects a strong commitment to integrity and compliance — qualities that resonate with both customers and regulators.
Establishing Effective AI Controls
The rapid expansion of AI use has prompted organizations to take a closer look at how they manage risk. Developing sound internal controls around AI starts with understanding where and how it is being used and identifying the potential exposure it creates. While SOC 2 categories — security, confidentiality, availability, processing integrity, and privacy — provide a high-level structure, AI oversight often requires a deeper level of scrutiny. When assessing AI governance, organizations should ask critical questions such as:- What business processes currently rely on AI, and is there full visibility into those systems?
- What types of risk do AI tools introduce — from model accuracy to data privacy?
- Does the organization have a defined AI data governance framework?
- How are model performance, reliability, and accuracy measured over time?
- Is there a monitoring process in place to detect model drift or bias?
- How is sensitive information protected within AI systems?
- Who is responsible for overseeing AI compliance and mitigating potential risk?