AI Governance Belongs In Your Organization

In the modern workplace, GenAI models have become powerful assets due to their ability to introduce efficiency, up level product innovation, and expedite how teams close the gap on competitors. However, these powerful tools also introduce significant risks related to data security and governance. Companies that aren’t actively figuring out how to govern the GenAI they’ve adopted will inevitably be left vulnerable. The risks that GenAI introduces are unavoidable and should be assessed and actively managed, rather than ignored. Ensuring that a company has stable and robust governance practices is the best approach to protect an organization's sensitive information and ensure compliance with relevant regulations, while allowing teams to use newer tools. In this blog, we’ll cover what AI governance is, why it’s important, ways to understand what risks your company is facing, and the necessary steps to mitigate them.

What is AI Governance 

Generative Artificial Intelligence (GenAI) governance is a set of frameworks, policies, and processes that organizations implement to govern the development and use of GenAI. Key aspects of GenAI governance include:

1. Regulatory Frameworks
2. Accountability Mechanisms
3. Intellectual Property and Ownership
   

AI governance addresses the inherent flaws arising from the human element in AI creation and maintenance. Since AI is a product of highly engineered code and machine learning created and used by people, it is susceptible to human biases and errors. Governance provides a structured approach to mitigate these risks, ensuring that machine learning algorithms are evaluated, updated, and actively monitored to prevent flawed or harmful outcomes.

What AI Governance Is Not

AI Governance is not security. It’s easy to assume they are one in the same but they should be introduced as complementary efforts to be layered because they solve different things. For example, mitigating prompt injection attacks is a data security focus, while ensuring regulation compliance is maintained is a governance focus.

AI Governance Is a Missing Piece to AI Adoption

According to IDC, worldwide spending on AI solutions will grow to more than $500 billion in 2027. Despite the growth, the lack of AI governance and risk management solutions is a major hurdle limiting further adoption.

Implementing a GenAI governance framework can save companies from costly mistakes. According to Gartner, companies that implemented a GenAI governance framework saw a 30% reduction in the cost of their AI programs. To understand the risks GenAI could pose to a company, teams should understand:

• What specific data is training the models
• Who is allowed to use models
• Who has access to the training platform the models are built on
• What is the purpose of the model
  

Once a team has a full view of what governance processes are needed, relevant departments like engineering, security, and privacy must align collaboratively on Gen AI use policies

The 5 Suggested Steps of a GenAI Risk Assessment

1. Perform a Data Risk Assessment

The data used to train GenAI models can introduce costly risks like:

• Privacy regulation violations: If the training data contains personal information or sensitive content, the model could leak or expose this data through its output which could cause violation of regulations like GDPR, CCPA, PIPLE, DPDP and others. some text
  • Regulation example: GDPR is a great example of where GenAI governance is needed, in the context of personal data protection and privacy. While GDPR is not built solely for GenAI, many of its provisions are directly relevant to AI systems, especially the ones focusing on the personal data of individuals within the European Union.
• Theft of proprietary information: Proprietary data like code, designs, or confidential documents used for training could be reconstructed from the model.
  

It’s important to assess the data that will be used to train models to accurately understand the risk landscape. This assessment also should focus on who has access to the data to adjust the outputs from the input. ‍

2. Threat Score End Users‍

Risk is heavily dependent on factors like who the end user is and what they're using. For example, an employee can use public GenAI models but so can malicious users, while private models should only be accessible by private license holders. Companies should know what different roles users have, thoroughly assess risks based on level of training, and implement appropriate guardrails like alerts and monitoring. Users should be scored on a few factors including but not limited to:

• Where the end users are based geographically
• What data do they have access to
• Where are they sending the data
• What data types does the company store
  

3. Understand Intended Use Cases

Knowing the intended use cases for using GenAI models helps to determine the level of the risk. Companies should asses the intended use of each GenAI model especially if used in:

• Business-critical use cases: This could include automating code.
• External use cases: Launching external facing products without proper review.
  

Identifying critical processes where AI will be used requires organizations to further assess risks like unsafe outputs, adversarial vulnerabilities, and lack of transparency.

4. Perform a Risk Analysis

With a full picture of the data, end users, and potential risks, companies  can perform a risk analysis to design a response plan. For example:

• Risk plot 1: Sensitive company data is unintentionally leaked when an employee uses a public GenAI tool without considering the appropriate guardrails.
• Risk plot 2: An internal person(s) leverages malicious inputs to training data to negatively impact the company.
  

Such plots should be ranked on like likelihood and potential impact – considering the specific data at risk, initial purpose, and external accessibility.

5. Assessing Control Effectiveness

Controls should be dependent on the potential risk they are tied to, with goals like preventing data leaks, securing access, and ensuring model reliability. Examples of controls to implement are:

• Periodic internal audits
• Discretionary access control (DAC)
• Internal training‍‍

Who Should Be Involved in Creating a GenAI Governance Program

Having clearly defined roles and responsibilities across an organization is key to implementing a successful governance program. Below is an example layout that highlights key roles and responsibilities.

Taking the Necessary Steps

GenAI is still evolving, and the associated risks will continue to progress. Given this, companies must implement ways to continuously monitor the use and risks of GenAI within their environment. This means maintaining effective guardrails tailored to the findings of periodically (eg. quarterly or bi-annually) performed audits that help to account for changes in the organization’s risk landscape and rogue AI implementations.If that sounds overwhelming, using a DFPM platform like Riscosity makes getting started with your GenAI governance strategy easy. Within minutes, teams will be able to control, manage, and monitor all data flows to AI tools, mitigating risks before they happen. Talk to our team to learn more.