How to Safely Integrate LLMs Into Enterprise Applications and Achieve ISO 42001 Compliance

How to Safely Integrate LLMs Into Enterprise Applications and Achieve ISO 42001 Compliance

Enterprise applications, whether on-premise or in the cloud, access LLMs via APIs hosted in public clouds. These applications might be used for content generation, summarization, data analysis, or a plethora of other tasks.

Riscosity’s data flow posture management platform protects sensitive data that would otherwise be accessible to LLM integrations. In this overview, we’ll take you through a set of key AI risks to consider – we’ll highlight how Riscosity minimizes these risks, and streamlines ISO 42001 compliance in the process.

Risk #1: Data Security Risk

API calls to public cloud-hosted LLMs will inevitably expose sensitive data if not properly encrypted or secured. While traditional data intelligence platforms are able to identify the location of sensitive data at rest, they’re unable to monitor and protect data as it leaves company servers. This is where Riscosity steps in.

Riscosity Data Security Risk Controls:

1. Ensure that all API communications are encrypted using industry-standard protocols (e.g., TLS).

• ‍ISO 42001 Mapping: Section A.4.5 System and computing resources, which includes encryption as part of ensuring the security of data and communications.

2. Implement access controls and API security measures, including authentication mechanisms.

• ‍ISO 42001 Mapping: Section A.9.2 Processes for responsible use of AI systems, particularly in relation to access control and secure API usage.

3. Regularly audit and monitor API traffic to detect and prevent unauthorized access or data exfiltration.

• ‍ISO 42001 Mapping: Section 9.1 Monitoring, measurement, analysis and evaluation, where regular auditing and monitoring of API traffic are emphasized for security.

4. Use rate limiting and other controls to protect APIs from abuse or excessive usage.

• ‍ISO 42001 Mapping: Section A.6.2.6 AI system operation and monitoring, focusing on operational controls like rate limiting to ensure secure and efficient API usage.

Risk #2: Compliance Risk

Data transmitted to public LLMs via APIs may violate regulatory requirements if not handled appropriately. Data privacy laws are already the norm worldwide. Enterprises must consider Europe’s GDPR, 17+ different US state level laws, Canada’s PIPEDA, South Africa’s POPIA, Japan's APPI and South Korea’s PIPPA just to name a few examples. Further AI specific regulations are already being put in place as well, starting with the EU AI act. 

Traditional third party risk management procedures are no longer adequate. Enterprises need an automated solution to ensure data being shared with AI tools is in compliance with local regulations.

Riscosity Compliance Risk Controls:

1. Implement data anonymization before transmitting data to public LLMs.

• ‍ISO 42001 Mapping: Section A.7 Data for AI systems, specifically A.7.4 Quality of data for AI systems, which includes data anonymization and tokenization as key controls.

2. Ensure that APIs are compliant with relevant data protection regulations, including GDPR, CCPA, and many more.

• ‍ISO 42001 Mapping: Section 6.1.2 AI risk assessment, where compliance with regulations is a critical aspect of the risk assessment process.

3. Regularly audit API interactions to verify compliance with regulatory requirements.

• ‍ISO 42001 Mapping: Section 9.1 Monitoring, measurement, analysis and evaluation, focusing on regular audits to ensure compliance with data protection regulations.

4. Comply with data protection clauses in contracts with API providers.

• ‍ISO 42001 Mapping: Section 8.1 Operational planning and control, where securing contractual agreements to ensure compliance is emphasized.

Risk #3: Cost Management Risk

Given the inherent marginal compute costs of inference for AI, enterprise LLM providers charge clients variable rates based on usage. As AI adoption balloons, enterprises must have clear visibility into costs and the ability to throttle usage in order to protect corporate budgets.

Riscosity Cost Management Risk Controls:

1. Implement usage monitoring and alerting systems to track API consumption and prevent cost overruns.

• ‍ISO 42001 Mapping: Section 8.1 Operational planning and control, where monitoring and controlling operational costs are key components.

2. Establish usage limits and quotas for API calls to control costs.‍

• ‍ISO 42001 Mapping: Section A.6.2.6 AI system operation and monitoring, where operational controls, including usage limits, are part of managing AI system operations effectively.

3. Regularly review API usage reports to identify and address any excessive or unnecessary usage.

• ‍ISO 42001 Mapping: Section 9.1 Monitoring, measurement, analysis and evaluation, emphasizing regular review and evaluation of API usage for cost management.

Risk #4: Operational Risk

Enterprises need clear visibility into the third party dependencies of their products. This is a general best practice, but it becomes particularly crucial in the context of genAI. The Riscosity platform provides a single source of truth for the dependencies of all internal applications.

Riscosity Operational Risk Controls:

1. Establish redundancy and failover mechanisms to ensure continuity of service in case of API outages.

• ‍ISO 42001 Mapping: Section 10.2 Nonconformity and corrective action, which includes preparing redundancy and failover plans as part of corrective action planning.

2. Regularly test and update contingency plans to address potential API failures.

• ‍ISO 42001 Mapping: Section 8.3 AI risk treatment, where testing and updating contingency plans are part of mitigating operational risks.

3. Monitor the availability and performance of external APIs to proactively manage potential disruptions.

• ‍ISO 42001 Mapping: Section 9.1 Monitoring, measurement, analysis and evaluation, where ongoing monitoring of API availability and performance is emphasized.

Riscosity’s core data flow security solution protects outbound sensitive data to any endpoint (whether it’s a genAI service or any other third party). Our browser-based governance suite now also makes it effortless to protect sensitive data from being shared with online services by employees. 

The reporting and rule-setting for both offerings is housed in a single, intuitive user interface. Admins are able to monitor and protect data in motion regardless of coding experience.

The end result for client organizations is the ability to fully capitalize on the cutting edge of AI tools, with the peace of mind that sensitive data will always be protected. Curious to learn more about how we can help? Feel free to reach out at sales@riscosity.com!