Intro
AI governance platforms are emerging as an important strategic technology trend. Last year, Gartner listed AI Risk Trust and Security as the Top Strategic Technology Trend for 2024. For 2025, AI Governance Platforms, specifically, are the second most important strategic technology trend.
Given this, you may be wondering what an AI Governance platform is. And it’s a good question. At this point, anyone who has AI transformation on their roadmap needs to know about AI Governance. It’s both a strategic and operational necessity.
But because AI Governance is a fairly new, and fairly broad, category - covering a variety of domains (not unlike AI technology itself) – keeping track of the space can get a bit confusing. The good news is, the principles that underpin AI Governance are fairly straightforward, and many companies and platforms in the AI Governance ecosystem can be understood through analogs and parallels in existing enterprise technology.
From our standpoint, AI Governance is about connecting the dots – but more on that in a later post. Our goal in this article is to give you a holistic view of the AI Governance space. For those looking for more depth analysis, we highly recommend EAIDB RAI Ecosystem report.
AI Governance Industry Background
The foundations of AI Governance have been around for a while, long before AI Governance was trending towards center stage. When the idea for Fairo was conceived in 2021, over a year before the company was incorporated, both the EU AI Act and the NIST AI RMF were in draft form. The policymakers and industry experts that created those documents were drawing from foundations in ethics, philosophy, and algorithmic bias. This is why you may have heard the term AI Governance frequently used around the terms Responsible AI (RAI) and Ethical AI (EAI), as they are very much intertwined.
Even though foundational work on AI Governance has been occurring for years, nobody - not even OpenAI - was really expecting what happened after November 30, 2022 when the first version of ChatGPT was released and became a viral mega-hit.
While questions about AI ethics have been pondered for nearly a century. In the context of modern AI/ML, and now Generative AI, these questions not only require answers but also solutions.
AI Governance platforms are, in part, how we think about the answers and solutions to these questions. But they are not limited to this. AI Governance also can extend out into general corporate governance, strategy, and operations. To make this clearer, let’s spend some time going over the different types of governance platforms within the ecosystem, and talk about how your organization might leverage them as part of your AI strategy.
AI Governance Ecosystem
The AI Governance Ecosystem is made up of many different categories, each drawing experts from different domains. To make the function of each domain easier to understand, we’ll highlight analogous domains as they relate to traditional software development when applicable.
AI Security
AI Security platforms focus on protecting AI systems from potential threats, such as adversarial attacks, model tampering, prompt injection / jailbreaking, etc. These platforms additionally aim to protect the overall integrity of an AI system. Just as cybersecurity protects digital assets, AI Security platforms are designed to defend AI models and data in ways that traditional security tools can’t, addressing the unique vulnerabilities inherent to AI systems.
The main concern about AI Security, currently, is that the practice of identifying and mitigating risks in AI systems is not yet a science. It’s fairly well known that LLMs are capable of producing long, sequential, chunks of training tokens (see NYT Lawsuit), and the risk associated with LLMs leaking sensitive/privileged information are still just too high given the current state of technology.
These reasons, in conjunction with the high-impact risks associated with privacy and data security breaches, are likely why AI Security is the fastest growing category in the AI Governance ecosystem.
Model Operations
Model Operations platforms, also known as MLOps, handle the task of deploying, monitoring, and maintaining AI models, similar to how DevOps software helps do the same with traditional software.
MLOps software can be a standalone tool/platform, such as Weights and Biases, or it can be fully integrated into a Machine Learning platform, such as Data Bricks, Google Vertex, or Azure ML.
There are also new companies entering MLOps tailored to the LLM and AI Governance ecosystem. These companies bring additional focus to metrics related to bias, transparency, and adverse impacts.
Steering / alignment-as-a-service is also becoming a highly demanded part of the AI pipeline. As small language models, fine-tuning, and bespoke alignment become more prominent, the Model Operations field will grow to encompass more than just experiment tracking, evaluation, and deployment tools.
AI GRC
AI GRC Platforms handle all aspects of AI Governance, Risk Management, and Compliance for companies. A good AI GRC platform will help organizations track all their AI Use Cases, Models, and Vendors from a risk and compliance standpoint, as well as ensuring that responsible and ethical AI principles are being upheld.
AI systems generally have to comply with a host of both internal and external regulations, policies, and guidelines. As AI technology is evolving rapidly, with new tools, technology, and use cases being released every week, so are risk and compliance standards.
A good, holistic, AI GRC platform will capture data on processes, procedures, model-performance, and posture for all AI systems. AI GRC platforms are generally designed to integrate with existing technology, in order to save time, not re-invent the wheel, and facilitate progress.
Companies are starting to introduce items in their contracts specifically related to AI GRC, making sure that their partners have adequate and appropriate controls in place. In many instances, companies can be liable for AI that does not conform to agreements.
Fairo falls into the category of a holistic AI GRC platform, meaning that our platform handles not only aspects of risk management and compliance, but also covers elements of AI trust, security and model operations. Fairo is unique among AI GRC platforms in that it has a product-team focus.
We believe that embedding principles and making progress by leveraging the product lifecycle / SDLC is the best way to implement AI GRC. Users of the Fairo can enjoy a platform that feels more like a product intelligence and operations management tool than just a place to hold compliance checklists / documents.
Data Operations
Data Operations platforms oversee the entire data lifecycle, from ingestion to cleaning to monitoring.
High-quality data remains to be the foundation of AI. Companies that invest in data governance, and data operations, will benefit from the ability to build better, more secure, and more robust AI systems.
AI Privacy
The privacy industry has taken to AI Governance as an early leader. Organizations such as the IAPP offer training and certificate programs for their members, as well as host conferences dedicated to AI Governance.
Established privacy platform providers, such as OneTrust, have added extensions to their platform focused on the privacy aspects of AI Governance.
In addition to the established privacy players, there are companies who focus on more tech-forward areas such as data de-identification/masking, synthetic data (specifically for use in software development) and PII detection.
Both established players and innovative startups are beginning to test the limits of their technology by adapting them to the world of GenAI and transformers.
Even with rapid technology development in GenAI and privacy, most companies are not fully convinced that GenAI systems are ready to be deployed to production in high privacy-risk scenarios. Most of the existing GenAI use cases are ‘low stakes’ chatbots with RAG built on top of non-sensitive documentation, not including personal data. Think like a first layer of sales or helpdesk. And while there have been a couple of cases of failures of these ‘low stakes’ systems (i.e. Air Canada, a local GM dealership), the risk of leaking sensitive data has not been the major concern.
Model Builders
The current top-flight of generative AI models suffer from a number of common flaws. Issues such as hallucinations, unpredictable output, and susceptibility to misuse are plaguing the industry.
Building models is not cheap. Training and infrastructure costs are eye watering. Also, ROI for foundation model developers may be somewhat elusive, especially when the cost of data is factored in. Despite these challenges, there are a variety of innovators trying to build models on the basis of ethical principles.
These new models leverage new architectures, optimization algorithms, infrastructure, and more to build a more sustainable and inclusive set of AI models. Models are designed for specific under-represented groups, languages, and use cases.
Specialization by specific industries is also taking place, particularly focused on looking at industry data that is private and sensitive. In other words, data that is not in the existing training set of the current leaders in Generative AI.
Legal & Consulting
Legal & Consulting services provide the expertise to navigate the legal and ethical complexities of AI Governance. Given the nuance, from compliance and IP issues to ethical frameworks, many companies are looking outside their existing law-firms and in-house counsel to find experts specifically focused on AI Governance.
Consulting firms who have generally focused on data protection and privacy are also beginning to focus on AI Governance as a newmarket.
Overview
AI Governance is an emerging field, with big strategic implications for your business. Like AI, AI Governance is multi-disciplinary. It breaks down silos and challenges the existing way we look at our organizations. However, just because AI is new, doesn’t mean AI Governance platforms need to re-invent the wheel. Many are building off of established technology patterns from existing enterprise technology, such as DevOps, posture management, security platforms, GRC, etc.
In the AI GRC category, Fairo provides a unique and holistic solution, covering a number of important use cases for any organization adopting or integrating AI solutions into their business, or with their customers. For more information on how Fairo’s holistic AI GRC platform can help you manage your AI agents, models, and use cases, reduce bias, risk, and hallucination, get in touch!
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