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Compliance in the Age of AI: Addressing Challenges and Embracing Innovation 

The use of artificial intelligence (AI) and machine learning (ML) tools has exploded recently. Open AI’s ChatGPT and DALL-E, Google’s Bard, and Midjourney have shown the world just a little of what AI can do.  

But while it’s fun to play around with these tools in your free time, many executives are wondering about the implications of AI for their businesses. In this article, we’ll address how AI can help companies with their compliance strategies and what new challenges AI presents regarding compliance and cybersecurity. 

First, let’s get clear on what we’re talking about when we say “AI.” 

What Is AI? 

Often, people use terms like “AI” and “machine learning” without knowing what they mean. That’s understandable considering how quickly these concepts went from science fiction to everyday life. 

Broadly, artificial intelligence refers to advanced computer systems that can simulate human intelligence. More specifically, much of today’s popular AI technology uses machine learning techniques to achieve this simulation. “Machine learning” denotes a computer’s ability to learn from examples. Humans must feed these computer systems massive amounts of data to train them.  

When trained appropriately, machine learning algorithms can sift through massive datasets to classify information, find patterns, and make predictions. Some ML systems can even generate new content with the information they’ve learned — hence “generative AI.” 

Applying AI to Compliance and Risk Assessment 

Because today’s AI and machine learning tools can ingest and analyze data so quickly, opportunities abound for improved business efficiencies. When it comes to compliance and cybersecurity, digging through company data to collect evidence for an audit or identify risks is often the most time-consuming task. As such, AI can come in handy in a number of ways. 

Cybersecurity 

AI can enhance traditional cybersecurity measures. Machine learning algorithms, for instance, can analyze patterns and anomalies in network traffic to identify potential security threats in real time. This can reduce response times to security incidents and mitigate risks more effectively. 

By streamlining security processes and providing real-time insights, AI tools support organizations in maintaining the stringent security and privacy requirements outlined in SOC 2 standards, such as regulating access controls and protecting sensitive data. 

AI can contribute to the development of an adaptive security posture, where security measures are dynamically adjusted based on new threats and compliance requirements. 

Continuous Monitoring 

AI tools can provide continuous monitoring of systems and data, ensuring a proactive approach to security and compliance.  

Continuous monitoring is crucial for maintaining compliance with standards such as ISO 27001, which emphasizes “continual improvement” in information security management systems. 

Data Privacy and Security 

Standards such as ISO 27701 focus on privacy information management systems. AI can assist in automating data privacy compliance efforts, such as data classification, and ensuring that personal information is handled appropriately. 

Machine learning algorithms can help identify and prevent unauthorized access to sensitive health information, helping healthcare organizations adhere to the HITRUST CSF

Businesses can enhance payment card data security by detecting unusual patterns and potential fraud in real time, aligning with the requirements of PCI DSS

The Limitations of AI for Compliance 

As this inexhaustive list shows, there are many ways businesses can harness AI to improve their compliance strategies and risk assessment processes; however, executives should build their AI strategies thoughtfully and gradually over time. Here are a few considerations to keep in mind. 

The Importance of Context 

Over-reliance on AI for compliance activities can lead to complacency and reduced human oversight. “While automated tools can process information at scale, they often lack the nuance and contextual understanding that human experts bring,” says Patrick Sullivan, VP of Customer Success at A-LIGN. In other words, AI offers many benefits, but it often requires human understanding to interpret data correctly. Running AI algorithms without appropriate oversight can lead to costly errors.  

The “Black Box” Problem 

Many sophisticated AI algorithms are considered “black boxes,” meaning that their decision-making processes can be challenging or even impossible to interpret. Compliance standards often require transparency and explainability, making it essential to ensure that AI decisions are explainable to stakeholders and regulators. 

Uncertain Regulatory and Legal Landscape 

Speaking of regulators, the regulatory outlook for AI is still evolving. Companies should stay abreast of changing regulations related to AI, such as the proposed EU AI Act and ISO/IEC 42001 (in draft form). Of particular importance for compliance experts, ISO 42001 provides organizations with guidance on managing risks related to AI systems, maintaining compliance with data protection requirements, and implementing AI controls. This standard is expected to go into effect in early 2024. 

Furthermore, determining accountability and liability in the event of AI-related errors or compliance violations can be complex. Organizations need to consider legal frameworks and contractual agreements to mitigate potential legal risks. 

Considerations for AI Implementation  

As businesses explore how AI can help improve operations, there are a few possible implementation concerns to take into account: 

Employee resistance: Depending on the industry and company culture, employees may be resistant to the adoption of AI, especially if there are concerns about job displacement. Building trust in AI systems and providing adequate training can be essential for successful implementation. 

Resource limitations: Although using AI for time-consuming tasks can feel like an obvious win, developing, implementing, and maintaining AI systems can be resource-intensive. Smaller companies may face challenges in terms of budget and expertise, potentially affecting their ability to comply with the latest standards. 

Maintenance: The rapid development of cybersecurity threats requires AI systems to adapt continuously. Failure to keep AI models updated and responsive to emerging threats can compromise the effectiveness of compliance efforts. 

AI and Compliance: An Evolving Relationship 

In summary, companies can use AI and ML tools to more quickly analyze data and identify security risks. With the right automation, organizations can improve their overall security strategy and better adhere to compliance standards such as SOC 2, ISO 27001, and more. Still, it is important to remember that AI is a new resource for many industries, and the unique risks AI itself poses are not yet fully understood. As such, organizations should proceed carefully and consult compliance experts to ensure security and compliance risks are appropriately identified and addressed.