April 3, 2025
3 Questions to Ask Before Purchasing an AI Data Governance Solution
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As AI becomes a critical component of business operations, it’s essential to ensure that AI systems are governed effectively. AI can help businesses to make more informed decisions, but any AI-driven choices can put the integrity of the brand at risk - unless leadership is intentional about first implementing an AI data governance solution.
Navigating AI data governance is complex. Companies must comply with evolving regulations, protect sensitive data from breaches, and ensure their governance frameworks can adapt to new risks. A one-size-fits-all approach no longer works—organizations need tailored solutions that align with their industry’s regulatory landscape and risk profile.
Before selecting an AI governance software, be sure to evaluate a few different options and compare their features, case studies, and the power of their solutions to each other. The wrong choice could lead to compliance violations, security vulnerabilities, and an inability to scale with future AI risks. To make an informed decision, start by asking these three critical questions.
SEE ALSO: 3 Hidden Risks of AI for Banks and Insurance Companies
1. How Does This Solution Ensure We Meet the Regulatory Requirements for Our Industry?
When using AI in insurance, finance, healthcare, or another highly-regulated industry, compliance with local legislation is imperative. Organizations in these sectors must adhere to strict guidelines on data handling, algorithmic transparency, and risk mitigation, or risk facing severe penalties.
For a robust AI data governance solution, compliance is the baseline for managing AI risks. Beyond current laws, vendors should also be prepared to explain how their platforms plan to adapt to emerging regulations, too. Look for these key features in an AI data governance platform:
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Automated compliance checks that continuously evaluate AI models against regulatory standards.
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Audit trails that document decisions, model changes, and governance activities for transparency.
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Policy management tools that allow organizations to enforce industry-specific governance rules and update them as regulations evolve.
2. How Does it Ensure Data Security and Privacy?
AI systems rely on vast amounts of data to inform their future output. Of course, when you’re working with the sensitive data of customers or patients (in healthcare), you inherently take on the responsibility of keeping consumer data secure. Without proper governance, sensitive data can be exposed to breaches or manipulated to create AI bias. For a large brand, the fallout from a scandal like that can have major financial and reputational consequences.
To mitigate these risks, an AI data governance solution should have robust security features, including:
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Encryption and access controls to restrict unauthorized data access and ensure confidentiality.
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Automated AI risk assessments that continuously scan for vulnerabilities in data handling and AI models.
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AI explainability and transparency tools that allow organizations to audit AI decision-making, ensuring compliance with ethical and regulatory standards.
A solution that prioritizes security and privacy protects sensitive data and builds trust with customers and other stakeholders, rather than ruining it.
3. Can it Scale and Adapt as Your Company - and AI Risks - Evolve Over Time?
In enterprise AI governance, it’s important to ensure that your organization won’t outgrow your AI monitoring platform. Your company isn’t the only thing changing, though. AI risks also evolve, and a robust solution should be flexible to adapt to both your organization’s growth and the changing environment. Additionally, large companies often test out AI systems in one department, then expand their adoption as they realize increased efficiencies and other benefits from the system.
AI risks are constantly shifting, influenced by factors such as new regulations, evolving cyber threats, and advancements in AI capabilities. A solution that works today may become obsolete if it cannot adapt to these changes. To ensure long-term effectiveness, an AI data governance solution should include:
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Modular frameworks that allow organizations to customize governance policies as their AI ecosystem grows.
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API integrations that enable seamless connectivity with existing enterprise tools and compliance systems.
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Real-time monitoring to detect AI risks, track performance, and generate alerts for proactive risk management.
Additionally, a governance solution should offer continuous updates and regulatory alignment, ensuring that businesses stay ahead of compliance requirements and emerging AI risks. Choosing a solution designed for scalability helps organizations future-proof their AI governance strategy.
Book Your Demo Today
If your organization is using AI but you don’t have a governance platform to go with it, we encourage you to begin evaluating solutions today. Book your free demo to see exactly how we can help you to build trust in your AI.