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The Rule Engine’s Function in Navigating AI Compliance

May 13, 2024

Business process automation (BPA) is a potent solution for streamlining tasks, increasing productivity, and optimizing business operations. When coupled with AI technologies such as GenerativeAI (genAI), natural language processing (NLP), machine learning (ML), and robotics process automation (RPA) its benefits are further amplified. 

As businesses embrace artificial intelligence (AI) to optimize their operations, there’s a lingering hesitancy around integrating AI into processes due to compliance concerns. But, in reality, a robust BPA platform equipped with a powerful rules engine can significantly contribute to AI compliance.

Primary reservations surrounding AI implementation include data governance, privacy, ethical use of information, auditing, and security. So, how can a rules engine lower associated risks? The following five examples explain.

Data Governance and Privacy

A rules engine plays a pivotal role in data governance and privacy by automating essential processes. It can efficiently classify and tag data based on predefined rules and policies, ensuring sensitive information like personally identifiable information (PII) or confidential data is identified and handled appropriately to meet compliance requirements.

Access control is another fundamental in governing data. With the support of a rules engine, access control policies can be effectively enforced, determining who can access specific data types and under what conditions. In environments requiring data consent, the rules engine can manage consent workflows, ensuring data usage aligns with individuals’ consent.

Since compliance regulations often require organizations to adhere to specific data retention and deletion policies, a rules engine streamlines the enforcement of these policies by automatically identifying data that has reached the end of its retention period and triggering the appropriate actions, such as archival or deletion. 

Issues of privacy are closely related to data governance, prompting the need for additional rules that help ensure data privacy. Rules may mandate the masking or anonymization of PII or sensitive data to protect individual privacy, or can be created to require privacy impact assessments to evaluate the potential AI system privacy risks and implement measures to mitigate these risks.

Because numerous data governance and privacy tasks lend themselves to automation, compliance and privacy are easier to obtain with a rules engine.

Ethical Use of Information

When it comes to ethical use of information, a rules engine plays a crucial role in upholding fairness, transparency, and adherence to ethical guidelines. By enforcing fairness criteria, a rules engine can prevent biases and discrimination in AI decision-making processes, ensuring equitable treatment across diverse demographic groups. 

Additionally, rules can be implemented to mandate transparency, such as providing explanations for AI decisions and disclosing data sources and algorithms. These measures enhance accountability and bolster trust in AI systems. 

Overall, organizations can leverage rules engines to strengthen their ethical frameworks and promote responsible and ethical use of information in AI-driven processes.

Auditing

Auditing is an important component of compliance, and here, too, a rules engine can automate processes to enable audit functions. By logging all activities and decisions made by AI systems, it generates comprehensive audit trails essential for compliance monitoring and regulatory reporting.

Rules can automate the generation of compliance reports based on audit trail data, providing insights into AI system performance, adherence to regulations, and mitigation of compliance risks.

Maintaining compliance requires routine assessments of risks. A rules engine can conduct automated checks to ensure AI systems comply with relevant regulations, standards, and organizational policies, flagging any deviations or non-compliance issues for remediation.

Security

Security is paramount in ensuring AI compliance, and a rules engine serves as a powerful ally in safeguarding AI systems and sensitive data against potential threats and vulnerabilities. The rules pertaining to data governance and privacy, previously mentioned above,  also extend to security. However, additional security measures can be implemented with a rules engine.

Rules can mandate the encryption of data, both in transit and at rest, leveraging strong encryption algorithms and protocols to protect sensitive information from unauthorized access or interception. By encrypting data before transmission and storage, the rules engine mitigates the risk of data breaches and unauthorized disclosures, even in the event of network eavesdropping or physical theft. This proactive measure ensures data confidentiality and integrity, protecting sensitive information against cyber threats and aligning with security standards such as ISO 27001 and PCI DSS.

Moreover, while a rules engine can help enforce regular updates and patches to AI systems and underlying infrastructure, ensuring that security vulnerabilities and weaknesses are promptly addressed and mitigated, rules can be configured to detect and respond to anomalous activities or potential security breaches within AI systems, triggering alerts and notifications for immediate investigation and mitigation. 

By analyzing system logs, network traffic, and user behavior patterns, the rules engine identifies deviations from normal operation and flags suspicious activities indicative of security incidents, such as unauthorized access attempts, data exfiltration, or malicious behavior. This early detection of security threats enables organizations to respond promptly, contain the impact, and implement corrective measures to prevent further exploitation or damage, thereby bolstering security defenses and ensuring compliance with incident response requirements outlined in regulatory frameworks like GDPR and NIST Cybersecurity Framework.

Bolstering a Compliance Solution

A dynamic compliance tool on its own, a rules engine also can be integrated with a compliance system, enhancing compliance management capabilities through automated monitoring, enforcement, reporting, and risk management processes. This synergy enables organizations to achieve greater efficiency, accuracy, and agility in meeting AI compliance requirements and maintaining a culture of compliance across the enterprise.

In summary, concerns around integrating AI tools with BPA can be alleviated when considering the ability for a market-leading rules engine like Decisions to reduce risks and automate processes to help achieve AI compliance. 

Want to know more about the Decisions rules engine and how it can enhance your confidence in utilizing AI?

Schedule a time to talk with our team.

Cecelia Troyan
Cecelia Troyan is a content strategist at Decisions. She shares the Decisions vision through the development and delivery of compelling and accessible content.

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