When developing AI solutions, you must ensure they are risk-free and trustworthy. Part of this is making sure they are secure. They should also be ethical, upholding human rights and privacy values.
For that, you need to know the AI governance principles that lay the foundation for governance frameworks. Let’s take a look at what these principles are and how you can implement them.
But first…
What is AI Governance?
Data governance is the strategy that ensures your business data is secure and managed according to the requirements set by privacy regulations. AI governance is the same thing, but for artificial intelligence systems, ensuring AI development aligns with regulations like the EU AI Act and NIST AI Risk Management Framework (AI RMF). It encompasses the policies, principles, and practices that form the basis of ethical and secure AI systems.
Why Is AI Governance Necessary?
AI is being used to take over and automate business processes. Some of these processes are low-risk, such as chatbots that handle simple queries from customers to alleviate the burden on customer care executives.
Other uses of AI, however, pose a higher risk.
AI Risks to Be Aware of
For example, AI tools are being used in healthcare to make diagnoses and recommend treatments. Human resources departments use AI to speed up the applicant screening process. Banks and other financial institutions use AI to determine whether a person should be granted a loan. Autonomous vehicles use AI to drive without requiring a great deal of input from the driver.
These applications fall under the life or livelihood category. If the AI malfunctions or gives out the wrong result, it could affect people’s health or their ability to get jobs or loans. Unfair outputs can also affect a person’s ability to apply for a job or buy a house.
Bias isn’t the only risk posed by AI models; There’s also the risk of privacy violations. Any sensitive or personally identifiable information (PII) in the model’s training data is at risk of being revealed in an AI system’s outputs, especially when it’s generative AI (GenAI). As such, you must have safeguards in place to prevent that from happening.
Other AI risks include:
- Lack of Transparency: If you don’t know how your AI model makes decisions, you can’t defend them, if challenged.
- Security Risks: If the model doesn’t have safeguards in place, it could be at risk of attacks and malicious manipulation.
- Data Risks: Overfitting or poor generalization happens when the training data is either not extensive enough or doesn’t provide the right context for real-world usage.
- Model Drift and Decay: Over time, the model’s data or logic starts deviating, leading to poor results.
- Ethical Misuse: While the model is ostensibly for a certain use, it might start getting used for another purpose that might violate people’s privacy.
- Existential Risk: There’s a chance that humans might lose control of AI if artificial general intelligence (AGI) develops goals that aren’t aligned with human values, or if it becomes capable of outsmarting its creators.
- Other Risks: These include job displacement, social manipulation, dependence on AI, and potential social inequality.
The OECD Principles of a Responsible AI Governance Framework
The aim of AI governance is to reduce or eliminate these risks. Instead of reacting to issues, it aims to anticipate and remediate them from the outset. As such, AI governance principles apply to the whole AI lifecycle, from design to development to deployment, and ultimately to decommissioning.
The Organization for Economic Co-operation and Development (OECD) AI governance principles are:
- Inclusive growth, sustainable development and well-being
- Human rights and democratic values, including fairness and privacy
- Transparency and explainability
- Robustness, security, and safety
- Accountability
Its recommendations for policymakers include:
- Investing in AI research and development
- Fostering an inclusive AI-enabling ecosystem
- Shaping an enabling interoperable governance and policy environment for AI
- Building human capacity and preparing for labor market transition
- International cooperation for trustworthy AI
AI Principles for Trustworthy AI Systems
These overarching OECD AI principles can be broken down into the following nine priorities:
Explainability
It’s important for an AI system to have open and explicit decision-making processes. Being able to explain how the system reached an outcome is important for several reasons, one of which is trust. Neither the stakeholders nor the users can trust a black box system. Explainability is essential for people to understand why the decision was made.
Explainability also helps prevent bias. If there is a logical flow of outputs that led to a conclusion, you can identify where a sensitive attribute contributed to it. Then, because you know exactly where the process started leaning towards discrimination (or even a poor decision that’s not a bias), your developers know how to fix it. Explainability makes debugging easier.
Finally, being able to explain how your system’s algorithms work makes it easier to comply with laws like the GDPR and the EU’s AI Act. These laws have a provision for “right to explanation” and algorithmic transparency. As such, being able to show how your system works keeps you compliant.
Accountability
Someone — a person — has to be responsible for a decision made by your business, even if it was AI-generated, especially if it has real-world consequences. You can’t just blame the algorithm and avoid consequences. If someone was harmed by an output from your AI system, they should be able to seek recourse. They need a point of contact who can answer their questions, correct the mistake, and pay damages.
When you know who’s accountable, it also gives you a clear line of responsibility. It’s not about assigning blame; it’s about where the system went wrong and who must fix it. This knowledge allows you to take remedial action quickly. It also gives you structure for oversight, audits, and ongoing risk management. And, again, since accountability is a requirement by law, it keeps you compliant.
Safety
An AI system must not cause harm to individuals, society, or the environment. It doesn’t matter whether it was intentional or unintentional, or if it was physical, psychological, financial, or social harm.
The idea of an artificial intelligence hurting humans is not new. In fact, Isaac Asimov’s three laws of robotics, which were a work of fiction, are being applied to real-world AI applications, because safety is important.
The idea of not causing harm extends beyond just the output of the model. Certain applications are considered against democratic values and human autonomy. For example:
- Monitoring people based on protected characteristics
- Using psychological tactics to affect people’s decisions
- Tracking and profiling with the help of facial recognition software
Since safety is such an important principle, it’s important to integrate it from the design and development stages, especially if the model is going to be used in applications that affect life and livelihood.
Security
It’s not enough to develop a model that is safe; bad actors can use various techniques, like adversarial attacks or poisoning attacks, to “infect” a safe model into giving out poor outputs.
Attacks like model inversion and data extraction can also be used to steal sensitive information and PII out of the model’s training data. This information belongs to the consumers from whom you collected the information, and as such, is protected by data privacy laws. If it’s determined that you didn’t have adequate security measures to protect this information, you could face penalties and legal action.
Data security standards — encryption, access control, and secure model training — can help protect you from such violations.
Weak security could also lead to your AI model being co-opted into performing unauthorized tasks. Regardless of who is responsible, if your AI model is used for nefarious purposes, it’s your name and reputation on the line.
Transparency
This might seem very similar to explainability, but it is actually a separate, overarching principle that covers more than just how the model operates. Where explainability focuses on how a model makes its decisions, transparency answers questions like “What is the system?” “Who built it?” “What does it do, and how openly is that disclosed?”
This principle encourages openness from the AI development stage all the way to deployment. To meet the requirements of this principle, you need to clearly communicate the system’s design, purpose, limitations, data sources, and who is accountable for its outcomes.
Being transparent helps you ensure external oversight and democratic accountability. It allows regulators, users, or independent researchers to audit, question, and critique your AI systems. This principle is especially important in public sector deployments, where AI should not undermine human rights or public values.
Fairness and Inclusiveness
Eliminating bias is an important part of ethical AI development. The model should not discriminate against people based on their characteristics. Its decisions should be fair, impartial, and equitable.
Fairness in AI is not just about neutral treatment — it’s about actively mitigating harm and ensuring that no group is systematically disadvantaged. That requires thoughtful choices in data selection, model design, and evaluation criteria.
Inclusiveness means actively considering and involving diverse perspectives, needs, and experiences throughout the AI lifecycle. It also means involving diverse stakeholders in the design and decision-making process. This might include affected communities, ethicists, accessibility experts, or civil rights advocates. Their participation helps surface blind spots and ensures the system works for a broader population, not just the dominant or majority group.
Reproducibility
In software testing, reproducibility is an important aspect. You should be able to get the same results every time a set of inputs is entered. It’s the same in AI model development. Reproducibility helps demonstrate that the correct — or incorrect — result was not a fluke. The logic is consistent, regardless of how many times you enter the query.
Reproducibility supports accountability and transparency. When decisions made by AI systems can be traced and tested, it becomes easier to audit their behavior. You can diagnose errors and ensure compliance with legal or ethical standards.
To create a reproducible system, you need careful documentation of data sources, model design, training processes, and system configurations. These make your AI development more rigorous and trustworthy.
Robustness
It’s not enough for an AI model to perform well under “perfect” conditions. It should be able to deliver consistent results under a wide range of circumstances. That includes when it receives unexpected inputs, when the data is noisy, or when its environment changes.
This is especially important in real-world applications like autonomous vehicles, medical diagnostics, or financial forecasting, where mistakes can have serious consequences.
Robustness is essential for trust and resilience. It helps ensure that AI systems don’t fail unpredictably, cause harm, or make erratic decisions when conditions shift — something that happens often outside controlled lab settings.
Ensuring robustness involves rigorous testing, stress simulations, adversarial training, and continuous monitoring to make sure the system can handle edge cases and recover gracefully from errors.
Data Governance
Effective AI governance starts with strong data governance. The quality, security, and integrity of the data used to train AI systems directly impact how fair, accurate, and accountable those systems are.
Without clear oversight of where data comes from, how it’s processed, and who has access to it, even the most sophisticated AI models are at risk of bias, breaches, or regulatory non-compliance.
Maintain Ethics and Governance in AI Initiatives With BigID
For responsible AI development, organizations need tools that go beyond basic data management. They need intelligent solutions that automate discovery, classification, and control of data across the enterprise.
BigID takes the guesswork out of data governance by providing deep visibility, policy enforcement, and compliance capabilities at scale. The platform’s approach to AI governance uses autodiscovery to locate and map your data, including all AI data and assets. It also classifies your data, helping you govern it appropriately.
This AI security and governance solution ensures that the data being used by your AI systems is cleaned, enriched, and curated. It monitors your systems for risks and remediates them so you can use your AI technology with confidence.
Interested in finding out more about how BigID helps you as an artificial intelligence trust, risk, and security management (AI TRiSM) solution — all while staying aligned with key regulatory frameworks? Schedule a demo today!
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