Data Governance Automation: Leverage AI as a Digital Gatekeeper

AI-powered data governance must become a core part of the enterprise toolset to counter content chaos and carefully manage information risk.

The impact of artificial intelligence (AI) on business operations has been the subject of great debate for years. Some see AI tools as a key driver of digital transformation initiatives enabling almost every aspect of business to be modernized. For others, AI is simply the latest overrated technology that promises the world but fails to deliver tangible results.

Whichever side you take in this debate, the reality is that the AI-powered software segment is poised to generate $62.5 billion in revenue this year – and that figure is expected to grow even further in the future.

AI use cases such as self-driving cars and virtual assistants are grabbing headlines, but one of the most down-to-earth yet powerful ways AI can add value to organizations is as a protector. By acting as a digital gatekeeper, AI can help protect what is arguably a company’s most valuable asset: its data.

See also: Real-time data management is vital for new compliance rules

Structured Data vs Unstructured Data

Not all data is the same and organizations need to manage different types of data accordingly.

Structured data is the raw, qualitative information that organizations rely on to run most business systems and is often what comes to mind when most people hear the word “data”. Structured data is typically stored in fields and tables within databases – for example, customer purchase price in a CRM system or vendor invoice amounts in an ERP system. The high degree of clarity in structured data makes this information relatively easy to control and keep secure.

Industry analysts estimate that structured data represents about 20% of the actual data that organizations store and manage. The remaining 80% of data is unstructured.

What is unstructured data? The simplest answer is that it contains anything that doesn’t meet the definition of structured data. Unstructured data includes documents, files, spreadsheets, presentations, images, audio and video files, and any other data that does not reside in a table, form, or database application.

The volume, variety and rate at which unstructured data is created is exploding – each individual generates around 1.7MB of data per second, with over 4.66 billion active internet users in 2022. Without careful management, this array of unstructured data can result in a chaotic content environment. However, uncontrolled unstructured content can also be fraught with risk. Everything from company secrets to your customers’ personally identifiable information (PII) can be (and often is) stored in corporate documents and content assets. Often this data does not have a clear owner, does not have an audit trail of access and modifications, and most likely will not be stored and secured appropriately. This lack of data governance can expose organizations to substantial compliance, privacy and legal risks – and open the door to potential financial, brand and reputational damage.

Taming the beast of unstructured data is no small feat. Modern businesses manage massive volumes of unstructured data. Studies indicate that the average business stores well over 300TB of data across its various systems, with more data being generated every day. Additionally, unstructured data is generally not sorted into consistent formats or file types, making even the seemingly simple processes of accurately identifying, categorizing, and organizing this information daunting and error-prone to most organizations.

Introduction to artificial intelligence

Today, many companies are using AI to automate the identification, organization, and enforcement of security provisions for unstructured data. AI tools and models can quickly process large numbers of files and documents and perform a wide range of activities to benefit the business. These benefits vary, but the process followed to deliver them generally breaks down into three distinct areas: discovery, classification, and quantification.

Discovery involves accessing files within an organization and performing simple analysis to identify the type of data the AI ​​is working with. Automated governance solutions can automatically discover data properties such as file type, size, location, user permissions, and any existing metadata. While this is only enough to create a basic profile of the data, this data fingerprint massively assists the AI ​​in the next step of the process – classification.

Classification is where the strengths of modern AI begin to show. Depending on the software used, the AI ​​model can be used to identify:

  • The type of document — for example, a contract or an invoice
  • The language of the document
  • If the document contains personally identifiable information (PII)
  • If a document contains sensitive company information

The classification and data extracted as part of the discovery phase provides detailed insight into what unstructured data exists within an organization and precisely what it contains. Yet, while some organizations simply want their governance tools to identify potentially problematic files as high-risk, powerful AI tools can do more than just apply a label.

Modern AI technology is smart enough to intelligently quantify the risks associated with certain types of data. It can identify specific data that is confidential, contains personal information, or requires special handling, but it can also apply context to determine how the data should be handled. For example, an Excel spreadsheet loaded with sensitive financial information may require additional redaction or security, while a shareholder report featuring elements of that same information will require different handling.

By quantifying the level of risk within a file without human intervention, organizations can gain insight into where they need to apply additional protection and trigger specific actions. For example, a governance team can put rules in place that direct an AI to delete PII in documents stored in insecure parts of the network or place specific contracts under the responsibility of a records management solution. .

Intelligently Automated Governance

Identifying the risks associated with unstructured data and applying proactive protection methods to counter those risks is not a do-it-and-forget-it project. Rather than being used reactively, AI-powered data governance must become a central part of the enterprise toolset – to counter content chaos and carefully manage data risks. ‘information.

While continuously monitoring and protecting corporate data, organizations can establish governance best practices, such as automatically identifying documents that should be treated as records or tagging all documents containing information about the staff and HR as “internal”. With these guidelines in place, an AI can track and measure adherence without user intervention. Every action, status and history can be tracked, including when, where and what steps were taken to protect content. This capability is increasingly important with respect to privacy regulations such as GDPR and CCPA and the need for organizations to understand what geography their data resides in at all times.

With AI acting as their digital gatekeeper, organizations can achieve continuous and automated data governance. The ability to uncover deep business intelligence, carefully control and govern content residency, and automate enforcement of policies and procedures is invaluable to organizations in the risk-laden business world in which we operate. proper permissions and offering proactive risk management and control, automated tools can forever change the way data is governed.

A company’s data is one of its most valuable assets. We wouldn’t let anyone into our physical offices without going through a guard – why should we treat our data any differently?

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