Understanding AI Governance: A Strategic Approach for Businesses

What is AI Governance?

At its core, AI Governance refers to a defined set of policies, practices, guidelines, processes, and rules an organization establishes to govern its internal use, development, deployment, and management of artificial intelligence (AI) tools and capabilities.

With such a governance framework, organizations are empowered to achieve the following objective:

A robust AI governance framework enables organizations to:

• Foster transparency, fairness, accountability, and data privacy in AI use
• Emphasize human oversight at critical decision points involving AI
• Align AI use and development with established ethical standards and regulations.

An organization can leverage its AI governance framework to ensure that its AI tools and mechanisms are used and developed in ways that drive efficiency, innovation, and productivity while upholding obligations around ethics and compliance.

AI Governance: A Practical Necessity

Consider the following scenarios.

Scenario 1

A hospital implements an AI diagnostics tool that provides doctors with an in-depth insight into medical conditions based on routine medical tests. The same tool recommends treatment options for doctors to consider when rendering their prescriptions.

One day, the AI tool recommends a highly unorthodox and unconventional treatment option that doesn't fully convince the doctor. He consults with his fellow colleagues and shares the AI tools' recommendations. The AI tool has had a perfect operational record. Hence, the hesitance on the part of the doctors to disregard its recommendations.

They share their concerns with the hospital's Medical Director and Chief Privacy Officer. Per the AI governance framework their hospital has deployed, they decide to conduct a thorough assessment of the tool. This assessment, in addition to all input and training data being properly controlled and governed, reveals that the AI tool has made the recommendation based on incomplete or obsolete data it had received about the particular patient.

Had the hospital not had the AI governance framework in place where one of the recommended practices is to audit AI tools for faults in datasets regularly, the doctor may have gone ahead with the incorrect treatment recommendation, putting the patient's life and well-being at risk.

Scenario 2

A popular theme park deploys AI-powered virtual assistants to enhance the visitor experience. To this end, visitors can use virtual assistants to gain real-time information about queue lengths, help with navigation, and personalized itineraries.

All runs well for a couple of months until, during peak season, visitors begin complaining about delays and inaccurate responses from these virtual assistants. Upon investigation, it turns out the virtual assistant system needed to be properly scaled for the heightened demand.

The system recommends implementing new measures and backend features such as capacity planning, load testing, and continuous monitoring to ensure the virtual assistants are well-optimized to handle the heightened workload without compromising their response accuracy. However, these steps bring an unexpected litany of expenses that the theme park wasn’t properly prepared for.

The two aforementioned situations represent two wildly different industries. However, the likelihood of AI adaptation is equally high in each. And though their use of AI capabilities is likely for different purposes, AI governance will be equally important in either case. More importantly, failure to properly grasp these challenges may end up presenting new problems down the road that an organization cannot properly address.

Any organization, in any industry, operating at any scale that deploys AI tools and mechanisms will require an equally apt AI governance framework in place to ensure it continues to leverage AI responsibly.

Failure to do so will result in inefficiency and an overall inability to put AI’s full potential to use.

The Importance of AI Governance

For organizations, it is critical to understand why AI governance matters. Here are some vital areas where AI governance can be of tremendous benefit to an organization:

a.  Transparency

Transparency remains a critical challenge within the AI industry. This is particularly important when considering that the AI models themselves are considered a "black box," i.e., outputs can be generated by feeding inputs, but there is virtually no way of knowing and understanding the logic that produced the output.

AI governance ensures organizations have complete and clear insights about the inputs fed into the AI models. Careful oversight of the inputs will aid the organization in proactively identifying biases, errors, and potential shortcomings in the outputs generated.

These insights can then be used for timely corrective actions.

 b.  Legal Compliance

For organizations, legal compliance will be the most complex aspect of AI Governance. Regulations are becoming more specific about obligations involving AI usage and deployment. However, simply understanding regulatory requirements is insufficient. The true challenge is implementing compliant practices and mechanisms across the organization.

A robust AI governance framework enables an organization to operate within legal and regulatory boundaries. This becomes increasingly important as AI adoption accelerates across industries. Regulatory non-compliance risks grow as AI integrates deeper into corporate functions.

Existing data regulations like GDPR and CPRA already mandate organizational compliance for collecting, processing, and storing data. Global AI regulations will likely outline similar concrete obligations to avoid penalties. An AI governance framework helps ensure all organizational activities and AI tools align with relevant laws. AI governance provides the practices and processes to embed compliance throughout operations.

 c.  Accountability

In 2019, a Tesla Model S had an accident that led to the deaths of two people. This led to a paper from New York University discussing who is ultimately responsible for AI outcomes.

Organizations across multiple industries have been leveraging AI for various purposes.

Hence, their usage has varying degrees of risk posed to the general public. A comprehensive AI governance framework is vital in such a setting as it establishes a clear line of accountability for the outcomes produced by the AI systems.

Such accountability would extend to all stages of an AI's lifecycle, from development to deployment.

With an ethical AI governance framework, organizations can prioritize ethical considerations and ensure a rigorous process is in place 24/7 that improves AI functionalities and ensures fairness, accuracy, and outcomes that align with their mission and values.

d.  Risk Mitigation

With AI governance, an organization can adopt a strong and proactive emphasis on identifying and mitigating several risks associated with AI deployment and usage.

From AI poisoning and data filtration attacks to prompt injections and data ownership issues, AI usage will present a spectrum of varying challenges for organizations. Organizations can deploy various methods and mechanisms to manage some of these risks.

These methods will likely require organizations to carry out extensive risk assessments to identify potential areas of concern and implement relevant strategies as part of their overall AI governance framework.

As a result, a robust and effective AI governance framework acts as a potent safeguard against the potential pitfalls of AI use and deployment.

Mitigating Risks and Managing Compliance

Here are some ways a comprehensive and effective AI governance framework can help mitigate the various risks and challenges associated with leveraging AI capabilities:

a.  Thorough Policy & Procedure Documentation

As explained earlier, AI governance is a set of rules, procedures, and policies that determine how an organization uses AI systems. An effective way to maximize the benefits of such policies and procedures is to thoroughly map and document all such resources and assets.

Such documentation must contain detailed outlines of all ethical standards, legal requirements, data usage guidelines, decision-making processes, and risk management protocols the organization intends to implement.

Doing so empowers an organization to ensure that its AI tools and systems selection align adequately with its values and policies.

b.  Regular Employee Training

Consistently organizing employee training sessions and seminars has long been considered a critical and proven strategy to ensure employees are well-equipped to deal with insider threats.

The same holds true when it comes to AI governance. Organizations must be proactive in organizing regular training and sessions that inform the employees about the AI ethics, practices, and digital hygiene they must practice while using AI tools to ensure their use strictly conforms with the ethical considerations and regulatory protocols required.

Not only does this help employees make informed decisions when using various AI systems and tools, but it encourages ethically compliant behavior across the organization.

c.  Clear Accountability & Ownership

Identifying and designating clear roles and responsibilities for all AI-related decision-making is vital to establishing a clear accountability and ownership chain. Doing so ensures that all AI-related issues and concerns can be raised with the relevant stakeholders and personnel with clear guidelines to tie individuals and teams to AI-related outcomes.

A clear accountability and ownership structure creates a heightened focus on ethical considerations while fostering organizational compliance with the established ethical guidelines.

d.  Establish Ethical Guidelines

As mentioned earlier, each organization's requirements and uses of AI tools and capabilities will differ. Hence, the ethical guidelines should reflect that.

Depending on how volatile and sensitive the information that AI systems have access to, organizations may need to develop AI development and usage guidelines that govern both the employees and the systems themselves.

e.  Data Privacy & Security Mechanisms

Apart from AI, data privacy and security have been the other critical areas where organizations have invested significant resources over the past few years. With AI systems becoming yet another factor to consider in their data management, organizations must ensure they have robust and effective data privacy and security mechanisms in place to protect the information accessible by AI systems adequately.

However, this remains highly uncharted territory. Upcoming AI regulations will clarify the operational obligations organizations must satisfy when balancing data privacy with AI usage.

f.  Access Governance

Implementing a strict combination of access controls and the principle of least privilege (PoLP) is necessary to ensure only the most relevant personnel and AI models are given access to sensitive information.

Such access can be monitored in real-time to protect against unusual behavioral patterns that may indicate malicious insider threats or instances of unauthorized access. Furthermore, all access permissions can be reviewed at regular intervals apropos to the context of the access. Doing so allows for dynamic changes in access permissions without creating excessive redtape.

g.  Dataset Validation

As effective and efficient AI systems can be, they can be only as good as the datasets they're trained on. Hence, the minutest of issues with the quality and integrity of the training data can have a devastating impact on the AI's performance.

Hence, organizations must adopt timely and rigorous data validation processes where any identified biases, inaccuracies, and anomalies can be addressed promptly. Doing so ensures the dataset's quality, contributing immensely to AI-generated outputs' accuracy.

h.  Automation Policy

As AI capabilities and functionalities continue to expand at an exponential rate, it is likely that organizations will consistently study and evaluate just how much of their operations can be automated. It seems only a matter of time until such capabilities are leveraged for decision-making purposes, where the AI systems would require extensive access to the organization's data.

However, simply limiting access of AI systems to critical data would be a blatant half-measure. Similar to leveraging access governance to sensitive data assets with respect to personnel within organizations, a comprehensive framework must be developed to determine which AI tools should have access to what data in addition to their permissions related to the usage of such data.

i.  Human-in-the-Loop Validation Process

In the short and medium term, humans must continue to have an oversight role in all AI decision-making processes. This is especially critical for decisions that affect humans more intimately, as identified in the AI diagnostics tool anecdote earlier.

The Human-in-the-Loop validation process would allow an organization to continue automating processes but with human oversight to review and override generated recommendations and outputs when their judgment is at odds with AI's.

Beyond being functionally important, it would make it easier for organizations to address some of the ethical concerns related to AI uses, particularly the question of who is responsible in case an AI error in judgment has real-life negative consequences.

Future of AI Governance: A Look Ahead

AI's influence will continue to grow. Organizations across various sectors and industries understand this and have invested significant time and resources in researching how these capabilities can be leveraged to their maximum potential.

However, alongside AI's influence, questions and concerns about AI's ethical and responsible use also continue to grow. These issues are further compounded by the fact that it remains a matter of time until AI regulations begin coming into effect, creating further responsibilities for organizations leveraging AI.

Hence, sound AI governance policies and frameworks are becoming increasingly pertinent. These will ensure organizations can continue making the most of AI tools and capabilities presently and are well prepared to oblige with any future AI-related regulations.

For this purpose, exploring how current proposed regulations lay the groundwork for ethical AI deployment and drawing parallels with past technological leaps, such as the Internet, can help organizations chart a clear course to meet the challenges and opportunities.

Regulations Paving the Way

AI regulations are proliferating across the globe. As covered in this AI tracker, an increasing number of countries have draft bills in place or are in the process of legislation along similar lines.

The European Union's AI Act, Canada's Artificial Intelligence and Data Act (AIDA), and the National AI Strategy in the United States exemplify the global regulatory efforts to establish guidelines for ethical AI development, use, management, and deployment.

Transparency and explainability will likely be a vital tenet of all such regulations, providing organizations with a roadmap on how to develop and instill appropriate measures in place to meet certain regulatory requirements that would, in turn, cultivate trust and confidence among users, stakeholders, and the public.

Additionally, organizations leveraging AI capabilities will likely be handling data in large volumes, meaning they'll also be subject to numerous global data regulations. The regulatory landscape is comparatively more established in that case, leaving organizations with a far more concrete idea of how they must act to ensure their use of data is regulatory compliant.

Lessons From the Past

AI governance is important. Organizations understand this. The real challenge is how to establish an AI governance framework that is effective and ethical in the short and long term. As mentioned earlier, AI-related regulations will begin coming into effect soon.

However, organizations cannot afford to adopt a "wait and see" approach. Most organizations are leveraging AI tools and capabilities now, meaning their efforts to create and adopt an AI governance framework must also begin now.

In such a scenario, looking to scavenge lessons from the past may prove highly beneficial. Reflecting on the meteoric rise of the Internet allows organizations a rare vantage point to navigate how the current AI revolution may eventually pan out.

After all, similar to AI, when the Internet came to the foray, there weren't any comprehensive regulations, resulting in several organizations falling victim to cybercrimes, data vulnerabilities, and privacy breaches when waddling through uncharted digital territories.

These challenges often had severe consequences for organizations with tremendous financial and reputational losses. However, in time, organizations learned the importance of having dedicated teams of experts to holistically study the possible risks an organization's digital structure faced, identify the most immediate issues, and address them.

This was before taking into account the various Internet-related regulations that came into place, such as the Children's Online Privacy Protection Act (COPPA), the Health Insurance Portability and Accountability Act (HIPAA), and the Gramm-Leach-Bliley Act (GLBA), to name a few.

Each of these addressed a specific industry, with subject organizations having a clear list of regulatory obligations that would not only ensure they had the best practices, mechanisms, and tools in place to ensure compliance but also lead to a uniform vision within the industry on how best to go about their daily operations while using the Internet.

Regarding AI, the script need not be altered too much. Several of the aforementioned best practices illustrate how an organization may take a proactive approach toward developing an internal AI governance framework. When concrete regulations do come into effect, they'll provide greater clarity, and organizations can adapt their practices per the regulatory guidelines.

How Securiti can Help?

Responsible use of AI capabilities has emerged as a vital strategic objective for organizations that have begun utilizing such capabilities within their operations.

However, owing to the sheer volume of data, organizations have begun realizing that the most effective and efficient way of creating a reliable AI governance framework is via leveraging automation.

This is where Securiti can be of great help.

Securiti is the pioneer of the Data Command Center, a centralized platform that provides contextual intelligence, controls, and orchestration for the safe use of data and AI. Organizations operating in various industries and sectors at different scales rely on Securiti to provide unified data security, privacy, governance, and compliance controls across hybrid multicloud environments.

The Securiti Data Command Centre is an enterprise solution based on a Data Command Center framework. It allows organizations to optimize their oversight and compliance with all major global data privacy and AI-related regulations.

Within the Data Command Centre, organizations gain access to vital modules and products that help ensure compliance with various regulatory obligations. For instance, the privacy notice module empowers organizations to have a dynamic privacy policy that reflects their most current data collection and processing practices. Additionally, the DSR automation module provides comprehensive and in-depth insights associated with all user data requests to ensure such requests can be honored per regulatory requirements.

Request a demo today and learn more about how Securiti can help your organization deploy relevant solutions critical to an effective AI governance framework.

Frequently Asked Questions (FAQs)

Here are some of the most common questions and queries you may have related to AI governance: