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Generative AI Governance: Essential Tips to Get Started

Ronan Grobler

Compliance Success Manager


GenAI has interceded and disrupted ‘business as usual’ at an unprecedented speed, simultaneously bringing incredible power but undeniable responsibilities. Sure, modern-day businesses are well acquainted with technological advancements. However, AI’s arrival (and implementation) has certainly caused a fair amount of whiplash, as some companies still try to wrap their heads around use, risks, and overall ethical governance. 

Yet, it’s undeniable that GenAI propels new product development on a business level and can hold unparalleled growth opportunities and benefits. However, for it to be truly successful (and sustainable), it must be deployed responsibly and ethically. 

Although the idea of corporate responsibility isn’t novel, it tends to get more challenging as GenAI starts flowing into a larger role in business operations. Hence, there is a growing need for and importance of Generative AI governance. 

So, to help organizations implement ethical GenAI governance while leveraging The Power of Gen-AI in Regulatory Compliance, we’ve compiled some of our essential tips for getting started.  

What is Generative AI Governance?

To kick off, let’s look at what Generative AI governance entails. GenAI governance refers to the set of principles, policies, and practices that are specifically designed to encourage and ensure the responsible use of GenAI technologies across the entire organization. 

It looks at defining standards, establishing guidelines, and implementing controls to steer the development and deployment of generative algorithms. It includes understanding the basics of Generative AI and the unique challenges posed by AI systems that can generate creative outputs autonomously.

Who is Responsible for Ethical GenAI Governance? 

The ethical governance of GenAI encompasses shared responsibility across various entities. Key players include government agencies, corporations, researchers, and individuals.

Not only do regulatory bodies play a critical role in developing and implementing legal frameworks to safeguard against bias, unfairness, and privacy violations, but the responsibility is shared amongst corporations that develop GenAI models and organizations and businesses that use them. 

The Five Pillars of Ethical AI Governance

Five overarching pillars encapsulate the ethical governance of AI. These pillars provide a foundational framework for both ethical AI development and deployment. 


Accountability plays a significant role in ethical AI governance. Accountability in AI specifically revolves around the obligation AI developers, deployers, and users have to take responsibility for the outcomes of their systems. 

This includes acknowledging any real or potential harm caused by AI systems and establishing mechanisms to redress and correct. Two surefire ways to ensure accountability include maintaining comprehensive audit trails and working within clear legal frameworks that outline the granular responsibilities and consequences of AI-related actions. 


Transparency and accountability go hand in hand – you can’t have one without the other. Regarding ethical AI governance, there needs to be clear and transparent communication about how AI systems work and the data that is used. Sure, GenAI has become renowned for its effortlessness. 

Still, the implications of its use need to be equally clear and easy to understand for all stakeholders, allowing them to make decisions about GenAI use while understanding the implications it could hold for users and the organization. To achieve transparency, humans must easily understand and contextualize AI systems, and open standards and platforms that facilitate understanding and collaboration must be encouraged. 


Privacy is the cornerstone of ethical GenAI governance and ensures that individuals’ data is safeguarded and compliant with user consent and data protection laws. It’s no secret that GenAI systems tap into vast amounts of personal data, which is why stringent data privacy and protection measures must apply. Organizations must clearly understand how the use of AI may impact any regulatory data privacy requirements and their compliance. 

Ultimately, if GenAI apps cannot guarantee data privacy, businesses will find gaining customer trust and safeguarding internal data extremely challenging. 


In a growing digital landscape, tech security is an all-consuming concern. Add GenAI to the mix, and anxieties around the threat landscape grow exponentially. The ethical governance of GenAI includes prioritizing security and the ever-present risk of data leaks or ransomware attacks. CIOs must understand and prioritize these risks and how using GenAI models may impact their information security standards. 


Explainability (also referred to as “interpretability”) is the concept that a machine learning model and its output can be explained in a way that “makes sense” to a human being at an acceptable level. Explainability plays a significant role in the ethical and fair use of GenAI. GenAI models and their use must be clearly communicated across all departments and users. Without correct (and ethical) use, GenAI tools rarely bring any benefits to an organization. This plays a particularly critical role in industries such as banking or healthcare, where the incorrect use of GenAI can easily include an inherent bias leading to actionable results. 

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Ethical Challenges in Generative AI Governance

There’s a reason they call Generative AI the “Wild West” of technological frontiers. Despite the common acknowledgment of ethical risks connected to GenAI, there are still a few operational challenges when it comes to its governance. Some of the most common challenges and risks include:

  • Intellectual Property and Data Leakage
  • Vulnerability to Security Breaches
  • Unintentional Use of Copyrighted Data

Implementing Generative AI Governance in Organizations

Identify (and understand) the foundation model for your GenAI app

Successful governance should start before any form of GenAI implementation even begins. Why? Well, you can’t fairly (or accurately) govern something you don’t truly understand. That’s why the very first step should be to identify and understand the foundation model of your GenAI app. A foundation model is the underlying model upon which the app is built. Each foundation model is unique and has its own capabilities and limitations. Some notable foundation models include Language Models (like OpenAI’s GPT series) and Vision Models, which specialize in image understanding and generation, and even foundation models tailored to specific domains, such as healthcare, finance, or legal industries.

Include key players and stakeholders

To implement ethical GenAI governance successfully, it’s essential that you get the right team members on board. Instead of starting from scratch, aim to involve key stakeholders in the governance process that already play important roles in governance frameworks. For example, privacy professionals will provide invaluable expertise regarding complex technological use cases and regulatory requirements. 

Equally important is your information security team, who will offer unique insights into how to proactively safeguard your data against breaches while ensuring the correct access controls and security measures are in place to address any vulnerabilities within the GenAI infrastructure. 

Evaluate your risk landscape

Speaking of vulnerabilities, each organization has its own unique threat and risk landscape. Therefore, within ethical GenAI governance, it’s imperative to have a thorough understanding of the risks involved regarding your specific model’s performance for your use case. This includes identifying any legal, cybersecurity, environmental, trust-related, third-party-related, privacy, or even business-related risks and then proceeding to establish and implement proper risk mitigations. 

Establish a Generative AI Governance framework

There needs to be a clear and mapped-out framework regarding GenAI use in your organization. This should include all the basic principles that are commonly found in compliance frameworks. Include everything from policies, designated roles and responsibilities, security controls, and security awareness training models, ensuring all relevant documentation is readily available for human operator challenge and validation, where necessary.

The Future of Generative AI Governance

Fortunately, businesses aren’t doomed to row without a paddle regarding the successful ethical governance of GenAI. In 2024, the EU is poised to set the stage for comprehensive regulation of generative AI. Additionally, two quintessential regulations, the General Data Protection Regulation (GDPR) and the anticipated EU AI Act (AIA), are expected to play a central role in influencing European markets and serving as a benchmark for global governance.

Globally, due to the increased adoption of Generative AI, there have been distinct regulatory responses to keep tabs on. Notable examples include the National Institute of Standards and Technology (NIST), which has introduced an AI Risk Management Framework.

Despite best intentions (and efforts), successful GenAI governance is near impossible without expert insights into your security posture, data, and overall information security landscape. 

Fortunately, you’re in the right place. 


Never Compromise on Compliance With Scytale

Ultimately, with every tool or app you add to your tech stack, it all boils down to whether or not you’re using, obtaining, safeguarding, and storing information and data ethically, responsibly, and securely. Naturally, this can be challenging without a dedicated compliance, privacy, and security team. Fortunately, we’ve got you covered. 

Whether you want to reap the benefits of effortless and continuous compliance or gain insights into your security and privacy landscape 24/7 while proactively mitigating information security risks – we’ve got you covered. 

Leave your security compliance to us, as we help you get compliant and stay compliant without breaking a sweat.

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