By ; Basel Khaled

IBM (NYSE:IBM) today announced the general availability (GA) of watsonx.governance in early December to help businesses automate AI lifecycle governance and proactively manage risk and compliance. This expands IBM’s industry-leading AI governance capabilities to help clients govern machine learning and generative AI tools, applications, and models in one place.

Watsonx.governance is one of three software products in the IBM watsonx AI and data platform, along with a set of AI assistants designed to help enterprises scale and accelerate the impact of AI with trusted data. The platform also comprises the watsonx.ai next-generation enterprise studio for AI builders and the watsonx.data open, hybrid, and governed data store.  

In the first release of watsonx.governance, clients using LLM models within watsonx.ai — including IBM-developed and various third-party models, such as Llama 2 and those from the Hugging Face community — will be able to govern them on the cloud. Consistent with IBM’s approach to open AI, IBM is expected to expand these capabilities in 1Q24 to allow clients to govern third-party AI models from any vendor — on cloud or on-premises — to orchestrate governance processes across their entire organizations.

Watsonx.governance also can help clients facilitate compliance with internal policies, industry standards, current and future regulation. IBM believes in regulation at the use case level, with responsibilities on companies to operate trustworthy AI. To do this, there needs to be an understanding of how and with what data models are trained, how they arrive at their recommendations, and whether they are routinely screened for harmful bias. These priorities — transparency, explainability, safety and fairness — are the foundation of multiple proposals advancing worldwide for the regulation of AI. IBM watsonx.governance is designed to help clients manage their AI and prepare to meet those regulatory requirements head on. 

IBM offers automated capabilities that cover all three major pillars of AI governance — lifecycle governance, risk management, and compliance. Expanded capabilities for LLMs include:  

• Monitor new LLM Metrics: Monitor and alert in both inputs and outputs of LLMs when pre-set thresholds are breached for quality metrics and drift, instances of toxic language — including hate, abuse, and profanity — and Personal Identifiable Information (PII).  

• Visibility into LLM development: Automatically collect information about the model building process, while explaining decisions to mitigate hallucinations and other new risks.   

• Transparency of AI Lifecycle for LLMs: Automatically document model facts across all stages of the lifecycle, monitor for drift for text models, and track health details such as data size, latency, and throughput to identify bottlenecks and compute intensive workloads.   

• Validation Tools for LLMs:  Enable prompt engineers to map LLM outputs to provided context/reference data for Q&A use cases to determine whether the LLM is appropriately influenced by the reference data to help ensure it is relevant to the output.   

IBM fosters open innovation and collaboration to help clients deploy AI in a transparent and responsible way. IBM Consulting helps clients scale responsible AI with both automated AI model governance and organizational AI governance that encompasses people, process and technology from IBM and strategic partners. Our consultants have deep skills in establishing organizational culture and accountability, AI ethics boards, training, regulatory and risk management and mitigating cybersecurity threats, all using human-centric design.

IBM’s commitment to trust and transparency forms the foundation for its products. Recently, it announced intellectual property protection for its for IBM-developed watsonx models. Watsonx.governance is a continued investment in helping to foster responsible AI practices across various business domains and industries.