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The Importance Of Data Governance And Security For AI Readiness

Originally posted on forbes.

Recently, high-profile breaches and cybersecurity failures have brought data governance and security to the forefront. Separately from these incidents, regulators have also applied growing scrutiny to enterprise data use as generative AI and other data products and technologies proliferate. Recent efforts from regulators include the Biden administration’s executive order in late 2023 and the pending Artificial Intelligence Act by the European Union.

Your organization is responsible for governing and securing its data to prevent misuse. This will only become more critical as your organization increasingly deploys predictive analytics and generative AI. A secure, well-governed data infrastructure ensures the safety and integrity of the data that feeds AI initiatives, enabling you to trust your models and bring them to market with confidence.

Ensuring proper data governance and security relies on the capabilities offered by the software and platforms constituting your data infrastructure. Without these capabilities, keeping track of your data can be a struggle, as will preventing data from being compromised and safely making data accessible to stakeholders. Business process innovations or organizational changes alone cannot provide these capabilities, especially given the sheer scale of modern data flows. Without data governance and security, brand risks, legal troubles, customer endangerment and compromised intellectual property are all possible concerns.

Principles Of Data Governance

Data governance pertains to internal data management and consists of ensuring observability, control and scalability.


Observability is an organization’s capacity to track, visualize and understand all of its data products, from tables and dashboards to predictive models and similar assets. This is commonly accomplished through capabilities like collecting logs and metadata from data pipelines, populating data catalogs, maintaining audit trails and tracking the lineage of data products.


Control is about limiting data access to only the necessary stakeholders. It is ensured through capabilities like the ability to create and assign roles with unique access privileges (role-based access control) and the ability to identify and exclude or obscure sensitive data like personally identifiable information (PII) through blocking and hashing and limiting platforms’ connectivity to external networks. The capabilities necessary to control data overlap considerably with those required for security.


Scalability involves enabling and maintaining observability, access and control as an organization grows its headcount, builds a more complicated data infrastructure and handles greater volumes of data. Solutions include programmatic control of data tools and infrastructure (like through an API), automated user provisioning with multifactor authentication and ensuring that different elements of the data infrastructure can communicate with each other.

Principles Of Data Security

While data governance largely pertains to internal data management, data security specifically involves preventing unauthorized access to sensitive data by external actors. This is typically accomplished through practices such as end-to-end encryption, purging data once it is no longer needed, anonymizing or excluding sensitive data from data repositories, private networking and deployment and maintaining data residency in specific regions.

Based on your industry and jurisdiction, you must ensure the vendors you partner with offer the necessary certifications (like SOC2, ISO 27001 and HIPAA). In general, security depends on allowing only the minimal necessary access privileges for different categories of stakeholders to perform their roles.

However your organization chooses to approach data governance and security, it will need to observe, control, scale and secure data through capabilities such as metadata logging, encryption, programmatic control and more. This conversation may involve technical stakeholders such as analysts and engineers, as well as your legal counsel. Based on my experience, I’ve found it is more practical to assemble a data infrastructure from software and platforms that are confirmed to natively support these capabilities as opposed to designing and building them yourself.

Secure Your Data, Secure Your Future

Poor data security and governance increasingly pose not only competitive but also legal dangers. It is more important than ever for companies to preempt potential trouble through robust data governance and security practices that safeguard customer and proprietary data.

The good news is that the fundamentals of governance and security are likely to remain the same despite the particulars of pending regulations. Data governance will always consist of observing, controlling and scaling data products and operations, while data security will always involve denying data access to unauthorized parties. Compliance with GDPR, SOC2 and other common standards fundamentally depends on an organization’s ability to demonstrate good governance and security practices.

Regulatory compliance aside, good data governance and security are essential, beneficial capabilities for your enterprise. It means the ability to track the provenance of your data products, which in turn means the processes used to create them are replicable and credible. With a clear lineage for data products, you can easily maintain a single source of truth and trust your insights. Of particular importance from a public-facing perspective, it also means the ability to understand and correct data products when they produce poor results.

As the potency of advanced analytics and AI grows, so will the importance of data governance and security and the overall stakes involved. It behooves your enterprise to build a secure, governed infrastructure, have critical conversations and carefully evaluate and select the appropriate tools and platforms. Are you ready for what’s coming?

Source: forbes

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