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In 2024, one can confidently assert that enterprise software development processes are generally established and mature.  Governance and standards for software systems have existed for many years and there is no shortage of guidance related to traditional software development. For example, ISO/IEC 12207 was first published almost 30 years ago and was the first international standard to define the governance of all processes required for the development and maintenance of software systems.

However, as AI system development and adoption accelerate at a pace we have not seen before, best practices related to data governance, security and risk management have often been overlooked, ignored or avoided. Data scientists have been able to fly low for a while, but it has now become imperative for organizations to address deficiencies in AI systems in the areas of discovery, documentation, testing, code quality, data governance and security.

Standards and frameworks are emerging to address many of these gaps. Thankfully, ISO/IEC 5338 establishes the common-sense standard that AI systems should no longer be isolated or excluded from software governance. AI systems are simply a new, albeit revolutionary, form or category of enterprise software. Organizations must integrate AI software development governance into existing software governance processes and best practices.

In this blog, we focus on the data to explore the intersection of AI systems and enterprise data governance. The objective is to provide a foundational understanding of how effective data governance is imperative when building or using AI systems.

What is data governance?

Data governance encompasses the practices, processes and standards designed to ensure the efficient and effective use of data within organizations. It aims to guarantee data quality, consistency, usability and security. For cybersecurity professionals, the concept of data governance is often overlooked. For some, it may not even be clear what exactly is meant by "data governance" and why it is crucial to AI security strategy. That's why it's important to call out those data governance elements that lay the foundation for effective cybersecurity and the security of AI systems.

In the world of information technology, data governance can be defined as the set of rules and processes that organizations use to make sure their information is kept safe, is accurate and is used properly. It's about making sure that the right people have access to the right data at the right time, while also ensuring that private information stays private and the data is used in ways that comply with the law and ethical guidelines.

Taking this a bit deeper, think about our transportation system. Let's compare how an organization governs its data to how society controls the flow of vehicles to ensure safety and order on the roads. Similar to traffic laws, data governance establishes the guidelines for how data moves within an organization and its technology systems. 

The transportation analogy stretches even further if you consider the following:

  • Traffic signs and signals: In data governance, these are the policies and standards that guide how data is used and who can use it. They tell employees when they can "go" and when they need to "stop" and check for further permission.
  • License plates: Just as each car has a unique identifier, each piece of data has its own metadata. This metadata describes the data, tells you where it came from and helps ensure that only the right people can access it.
  • Driver's license: In the same way a driver must be licensed to operate a vehicle, data governance ensures that only authorized personnel can "drive" or use the data. This helps prevent accidents like data breaches or misuse.
  • Road maintenance: Just as roads need to be kept in good condition, data governance involves keeping data clean, up-to-date and in good shape for when it's needed.
  • Traffic cops: In data governance, these are the roles and responsibilities assigned to people who enforce the rules, such as data stewards and data managers. They make sure everyone follows the data rules, and they take action as needed when rules are broken.

In an era where data is a critical asset for every organization, effective data governance is essential — especially in the context of AI adoption. Data governance is not just about managing data; it's about ensuring data quality, security, compliance and proper classification. 

Below, we explore how data classification, security, compliance and AI technology contribute to a robust enterprise data governance strategy and why comprehensive data governance is pivotal to the success of your AI strategy.

Applying data governance to AI systems

While data governance is critical across enterprises and applications, it simply cannot be overlooked, ignored or skipped when building AI systems.

Let's consider another analogy. Imagine your new AI system is actually a state-of-the-art smart home that you've been commissioned to design and build. Your client wants the most futuristic, intelligent house ever — a true AI-powered smart home that learns from occupants and adjusts to their needs in real time. In this thought experiment, we can think of data governance as the blueprint and building regulations for this ambitious project.

  • Blueprint: Just like a blueprint that illustrates where every wire, pipe and support beam should go, data governance maps out where data should be placed, who can use it, and how it should be handled. Without a blueprint, your final smart home might have wires crossed and leaking pipes! Similarly, without data governance, your AI might make decisions based on incorrect or inappropriate data, leading to inaccurate or biased results.
  • Foundation: Just as a house needs a strong foundation to stand on, AI systems need high-quality data to function correctly. Data governance ensures the data that AI systems learn from is accurate, relevant and reliable — essentially, it checks the structural integrity of your building blocks.
  • Construction crew: In construction, there are specialists for each job and everyone needs to know their role to work effectively. In data governance, roles are defined for who can access data, who can change it and who ensures responsible data use. If everyone on the job site did whatever they pleased whenever they wanted, you'd end up with chaos; similarly, if data roles are mismanaged, your AI could "learn" all the wrong lessons.
  • Building codes: These are regulations that ensure safety and compliance in construction. In the data world, data governance ensures that all the laws and ethical guidelines are followed, much like ensuring your smart home doesn't violate any privacy laws or ethical boundaries.
  • Security system: Installing a top-notch security system can protect your home from intruders. Similarly, data governance includes safeguards like encryption and access controls to protect your data from hackers and leaks. Without it, your AI's "home" risks easy break-ins that can compromise the privacy and security of your data.

Your goal is to complete your smart home based on good design principles, a strong foundation built to code by a well-qualified and capable crew, featuring built-in security by design. The result is a living space that's innovative, useful, safe and private. Thinking of your new AI system as the intelligent home of your data can help you appreciate the importance of robust data governance as a foundation.

Data governance is critical in AI strategy

Data governance is a crucial component of any AI strategy as it underlines model accuracy, risk reduction and trust:

  • Foundation for accurate AI models: The quality and structure of data directly affect the effectiveness of AI models. Well-governed data ensures more accurate and reliable insights.
  • Mitigating risks: Proper data governance mitigates risks related to data privacy, security breaches and compliance violations.
  • Building trust: At a time when data privacy concerns are high, robust data governance can foster trust among your customers and stakeholders.

3 pillars of data governance: Classification, security and compliance

Now that we have a solid foundation of what data governance is and why it's critical to AI success, let's explore the three pillars of data governance for AI (classification, security and compliance) and how to successfully structure your program for AI systems.

Pillar 1. Data classification: Organizing data for efficient use

Data classification stands as a cornerstone in the towering structure of AI data governance, serving as both the foundation and the blueprint for the elaborate systems that hold the world of AI together. The meticulous process of organizing data based on sensitivity, importance and type is not just a preliminary step; it is a continuous, dynamic process that enables AI systems to operate with precision and integrity. 

Let's delve deeper into the world of classification and its pivotal role in AI data governance:

Prioritizing data use

While data is the lifeblood of AI systems, not all data is created equal. Like sorting through gems, data classification helps in identifying which datasets are invaluable and which are less critical. This is not merely a process of elimination but a sophisticated method to align data with the strategic goals of AI models. It answers critical questions: Which data will train the AI to recognize patterns? Which datasets will refine its learning? By highlighting the most impactful datasets, data classification ensures that AI models are not just learning but evolving.

Ensuring data quality

The phrase "garbage in, garbage out" traditionally implies that the output quality of a system (like a computer program or AI) directly correlates with the quality of the input data. However, when it comes to AI, the saying is sometimes modified to "garbage in, garbage amplified" to reflect the fact that AI systems, particularly those involving machine learning, not only reproduce input errors but can also magnify them. This is because AI systems can learn and perpetuate the biases or inaccuracies in the training data across all subsequent analyses or decisions, often in a way that is not immediately obvious, thus "amplifying" the errors.

Quality data is the non-negotiable input for the creation of effective AI models. Through data classification, organizations can segregate data of high accuracy and relevancy from other data. This ensures AI models are trained on the best available data, resulting in more reliable and insightful outcomes. 

The role of data classification here is akin to a master chef selecting only the freshest ingredients for a recipe; the quality of inputs directly determines the quality of the output.

Facilitating compliance and privacy

In the digital age, data is not just an asset — it's a liability if handled incorrectly. Data classification is instrumental in recognizing sensitive information that might fall under specific regulations such as GDPR, HIPAA or CCPA. By identifying and categorizing data according to its privacy requirements, organizations can devise tailored security measures to protect personal information and other sensitive data. This is not just about avoiding fines; it's about upholding the trust of customers and maintaining the integrity of AI systems.

Pillar 2. Data security: Protecting the lifeline of AI

In the burgeoning AI landscape, where data is not just processed but dissected, analyzed and reassembled to form decisions, the need for impeccable data security cannot be overstated. Strong data security in AI systems is multifaceted, involving encryption, access control, authorization, integrity and regular audits. These elements work in concert, safeguarding the lifeblood of AI systems. Protecting data from unauthorized access, breaches and leaks is crucial. Data security is indispensable and should be non-negotiable for AI, as AI systems often process sensitive information, making them prime targets for cyber threats. 

Key elements of data security include:

Encryption: The cipher of data safety

Encryption is akin to an enigmatic language that only a select few can understand. It transforms valuable data into an unreadable format that is impenetrable to anyone who does not possess the key, ensuring that even if data falls into the wrong hands it remains undecipherable and therefore useless. In AI systems, where data can be extraordinarily sensitive, encryption is not optional: it's the basic dialect of security.

Access control and authorization: The selective gatekeepers

Access control and authorization are the vigilant gatekeepers that manage who enters the data realm and what they're allowed to do once inside. These mechanisms ensure that only verified and approved individuals can interact with an AI system's data. They are the bouncers at the club, checking credentials and granting entry only to those on the list. In the realm of AI, where data misuse can have far-reaching consequences, these gatekeepers are crucial.

Data integrity: The unyielding backbone

Integrity in data security refers to the assurance that information has not been altered in an unauthorized manner. For AI systems, which rely on data to make decisions, integrity is the backbone. It guarantees that the data used to train AI models is pure, uncorrupted and available as intended. Any compromise in data integrity can lead to flawed AI behavior, making its safeguarding a non-negotiable aspect of data governance.

Regular audits: The health check-ups of data security

Regular security audits are like systematic health check-ups for an AI system's data security. They involve thorough system inspection to ensure all security measures are functioning optimally and no vulnerabilities are left unchecked. These audits are proactive steps in identifying potential security threats before they manifest, embodying the adage "prevention is better than cure."

Beyond the basics: Advanced data security measures for AI

AI systems often require more advanced data security measures due to the complexity and sensitivity of the tasks they perform. This may include behavioral analytics to detect unusual patterns that could signal a security breach, or the use of homomorphic encryption that allows data to be worked on while still encrypted, offering a new frontier in data security.

The human element: Training and culture

It is crucial to acknowledge the human element in data security. No matter how advanced security systems are, they can be compromised by human error or malice. Therefore, a robust data governance program for AI also involves comprehensive training for personnel and fostering a culture of security awareness.

Compliance ensures that AI work adheres to legal and ethical standards, including regulations like GDPR and HIPAA, which dictate the handling and protection of sensitive data. As mentioned earlier, existing compliance requirements for enterprise data apply equally to AI systems, which qualify as software systems. 

Additionally, AI-specific regulations are rapidly evolving. Organizations should be prepared to address future regulations specific to AI systems using the following best practices:

  1. Mapping principles to written procedures: Your AI governance program should go beyond abstract principles and be embodied in concrete, written protocols and procedures. This includes aligning your AI practices with Responsible AI principles like fairness or explainability and updating these as necessary based on changes in AI strategy or regulations​​.
  2. Establishing multiple lines of defense: Implement a risk management strategy that involves multiple layers of defense. This includes teams at different stages of AI development or deployment, each responsible for managing specific risks and driving ethical AI behavior across different defense lines​​.
  3. AI literacy and responsible AI training: Cultivate AI literacy across your organization and provide role-based training. This helps employees understand the implications of interacting with AI systems in their specific roles and fosters a responsible AI culture​​.
  4. Data governance and AI integration: Integrate AI into your existing data governance frameworks, considering compliance requirements, ethical considerations, and potential risks related to biased or discriminatory outcomes. This involves careful data management, including classification, lineage tracking, access controls and retention policies​​.
  5. Regular monitoring and auditing: Continuously monitor and audit your AI systems to detect and mitigate potential risks and ensure compliance with established regulations and guidelines​​.
  6. Staying informed about regulatory changes: Keep abreast of evolving data governance requirements and regulations. This proactive approach involves participating in relevant industry dialogues and fostering collaborations with legal and compliance experts​​.

By implementing these best practices, you can prepare your organization for compliance with both existing and future AI-related regulations. This proactive approach to AI governance and compliance not only ensures adherence to legal requirements but also builds trust and transparency in AI systems.

Using AI to enhance data governance

Up until this point, our message has been focused on building or applying your data governance processes to new or existing AI systems. However, it's also beneficial for enterprises to consider how to take advantage of AI to enhance their overall data governance processes. 

As we continue to cover AI security topics, WWT's teams will generally delineate between the security of AI and security by AI. In the case of data governance, both paths apply. AI tools and capabilities are available to enhance your overall data governance process, for all technology systems. Areas where AI will improve or augment existing data governance include:

  • Automated data classification: AI algorithms can categorize large volumes of data efficiently and accurately.
  • Enhanced security protocols: AI-driven security systems can predict and identify potential threats more swiftly than traditional systems.
  • Data quality management: AI can help clean, process and ensure data quality — all vital for any AI-driven initiative.

Security by design requires data governance by design

Data governance in AI is a multifaceted endeavor that includes data classification, security and compliance. By effectively managing these aspects, organizations can optimize their AI initiatives and maintain trust, compliance and a competitive edge. 

For enterprises leveraging AI systems, integrating data governance from the start is crucial. This involves developing comprehensive data management policies, investing in advanced security infrastructure, training employees on data best practices and fostering collaboration across departments. Conversely, the integration of AI in data governance processes streamlines and enhances these efforts, illustrating a synergistic relationship where AI and data governance mutually reinforce each other. Recognizing the critical role of data governance is the first step toward AI maturity.

In closing, as we usher in an era where AI's influence is inextricably tied to the data it learns from, the need for stringent data governance becomes the bedrock of technological integrity and trust. It is no longer sufficient to simply manage data; we must apply rigor and discipline to its governance, ensuring that we uphold standards of accuracy, security and ethical compliance.

As stewards of this digital frontier, we must embrace the reciprocity between AI and data governance, allowing each to inform and enhance the other. Our collective action in weaving these threads into the very fabric of our AI initiatives will safeguard our data's sanctity and uphold the standards we value. Many will ignore or avoid this challenge, but WWT is committed to innovating and helping our clients establish data governance as the bedrock of their AI-powered future. Let us help get started on your AI security Journey.  WWT's AI Security Strategy Accelerator service will provide you with a vision for fully architecting your AI security program.