Governance for compliance and explainability

While safety, trust, and ethical concerns with LLMs continue to evolve, a parallel focus remains on the effective governance of AI applications. In addition to addressing the technical vulnerabilities of LLM applications, it has become increasingly important for organizations to address some key governance factors:

Regulatory compliance: Existing enterprise compliance policies must be adapted in order to successfully navigate the complex landscape of AI regulations concerning data privacy and protection. A holistic data governance policy enables organizations to not only be in compliance with existing regulations but also be better prepared for any future AI regulations.

Risk Management: Implementation of the guardrail modules, aligned with organizational information security policies, provides an effective set of tools for managing risks surrounding the use of LLMs. Mechanisms such as isolated sandboxes for experimentation foster innovation while managing the risks of unauthorized use or exposure of sensitive production data and providing greater due diligence.

Visibility and discovery: Implementing sensible “AI Use Policy” within the organization, along with effective auditability of the systems utilizing LLMs, provides greater visibility into AI usage patterns and cost management. Proper logging of AI model input and output helps with the explainability of model behavior over time within the system.

Change Management: By implementing appropriate change management processes, establishing Change Advisory/Control Boards (CCB, CAB), and implementing thorough testing mechanisms and cadence, organizations can have better visibility into model version changes and any new and emerging model behavior within their LLM applications. This allows for better change control, fewer accuracy issues, cleaner data in downstream systems, and a lower level of system disruptions.

Conclusion

Generative AI has opened many doors to enterprise innovation and is redefining how organizations view, plan, and execute their transformation strategies. While AI still holds impressive growth statistics in the industry, a 2024 Generative AI Global Benchmark Study found that 63% of companies plan to increase AI spending, which is down from 93% in 2023. The survey highlights data security concerns as one of the top concerns for businesses looking to implement generative AI applications. It also highlights that organizations still have long-term plans for substantial investments in AI, underscoring the need for better guardrails and governance mechanisms. As we look to the future, it is undoubtedly clear that the infusion of generative AI into enterprise systems is inevitable, which presents itself as an opportunity to establish industry-standard guardrails and governance frameworks that ensure responsible innovation, protect stakeholder interests, and maximize the transformative potential of these technologies while mitigating associated risks.

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Guardrails and Governance: Navigating the Complexities of Generative AI in Enterprise Operations

By Anjanava Biswas on

September 19, 2024

Navigating the Complexities of Generative AI in Enterprise Operations Private data detection (GM01): This module involves the detection and prevention of the propagation of data containing personally identifiable information (PII), protected health information (PHI), proprietary information, and intellectual property data. Techniques such as anonymization or pseudonymization are some of the most common methods to mask sensitive information. This module essentially operates on the principle of least privilege (PoLP) when it comes to the propagation of private data within LLM applications, depending on the use case.

Toxic content prevention (GM02): This module involves the detection and prevention of toxic and harmful content. It focuses on preventing the propagation of toxic, offensive, and harmful content within the LLM application.

Prompt Safety (GM03): Involves detection of prompt intention to prevent prompt injection attacks, malicious, or irrelevant data being sent to the LLM. It focuses on implementing scrutiny of the data being fed into the LLM to prevent unauthorized manipulations of the model's behavior, potential security breaches, and the generation of harmful or inappropriate content.

Human feedback and data sanitization (GM04): Specifies periodic human evaluation of the data generated by the LLM and re-configuring the system in order to increase accuracy and prevent occurrences of LLM hallucinations or confabulations. It also involves the implementation of model output sanitization mechanisms to protect back-end systems from XSS, CRSF, SQL injection, and other similar types of attacks.

These guardrail modules address five of the ten OWASP LLM application vulnerabilities for model consumers. It’s important to note that while these guardrail modules are highly relevant for model consumers, they also address a few vulnerabilities for model providers.

At a technical level, a framework proposed in 2023 suggests utilizing a combination of rule-based heuristics, and smaller open-source transformer models to perform private data detection, anonymization, and redaction. The framework proposes training smaller transformer models using an open-source multi-lingual toxic comments dataset to perform multi-label classification for toxic content to detect and label text as hate speech, slur, profanity, abuse, and so on, and assign a score to each of these classes. Subsequently, the framework also proposes a combination of vector similarity and a binary classifier mechanism to detect prompt intention, and classify prompts as either suspicious or non-suspicious. The study suggests that, compared to training LLMs, smaller transformer models can be trained and deployed for the purposes of guardrail module operations on cost-effective and cheaper consumer hardware.