In many ways, compliance is the cost of doing business. It doesn’t generate revenue, but it is an essential part of operating effectively as a business today. Whether it’s industry specific regulations, or the standout regulation of our time – GDPR - we are all acutely aware of the damage, both reputational and financial, that non-compliance can cause.

GDPR has equipped employees across industries with an appreciation of the context, usage, and security of data, but there is another factor that is essential for establishing an effective data strategy, which is data discoverability. To ensure regulatory compliance, data must not only be secure, it must also be discoverable so that compliance personnel can locate all information needed to prove compliance.

Increasingly, AI tools are being harnessed to automate workflows and governance, but such capabilities can only be delivered when a strong data foundation is in place.

What’s in a label?

The key risks when it comes to compliance lie in exposing or sharing the wrong information, or failing to produce the desired information when required by auditors. To minimise these risks, it is essential that all information within an organisation’s systems is made discoverable and delivered in a user-friendly format.

One of the first steps to enabling this is the process of data classification. For example, invoices contain sensitive financial information so are a prime example of documents that require strict governance protocols, such as those around access and shareability. These rules can, of course, be applied on an ad-hoc basis, but this is an extremely inefficient model and prone to human error.

A much more robust model is a system that inherently understands which documents are sensitive and automatically applies governance rules to them. In short, a system must understand the classification of each data asset to understand its risk profile—and it’s here where AI tools can deliver truly transformative value for organisations.

Through the use of classification machine learning models, a data asset that is of regulatory significance can be surfaced and automatically made compliant for its entire lifecycle. While this will require some pre-labelling work, in which sensitive assets are manually labelled - or automatically labelled through clustering models - to train the classification model, the long-term benefits for organisation are clear. One only needs to consider the time cost of the average data subject access request (DSAR), which can be anywhere between £3,000 and £6,000 to realise the efficiency and cost-reduction dividends of more advanced data discovery.

Uncover hidden risks

Classification algorithms are a great way to automate compliance rules for data and information across an organisation. Put simply, if a document looks like an invoice, it will be classified as one with a high degree of accuracy. But if a regulator requires multiple documents relating to a specific asset be collated, classification will only get you so far.

For example, within asset heavy organisations, every single site will often have a number of documents that will be needed to ensure compliance, such as maintenance history reports and schematic diagrams. To ensure that each asset is compliant, companies must be able to surface all the relevant documentation, but doing so with ease for potentially hundreds of assets presents a significant logistical problem.

Building on the work of the classification models, named-entity recognition can be used with machine learning models to search and discover all documents that contain a specific asset code, bringing unstructured data into the compliance automation process.

Know the rules

Of course, before embarking on any machine learning project, it is essential that compliance requirements are fully understood. It’s easy enough to make a model that will search for asset codes, but when there are specific regulatory nuances to consider, subject matter experts must be consulted for each area of compliance.

One compliance model will look very different to another when it relates to an entirely separate regulatory framework. Water companies, for example, must ensure compliance with specific regulations and manufacturers must comply with a multitude of ISO standards for their products. Organisations may also have their own compliance policies that relate to business best practices or mission statements around the usage of data.

In each case, an initial discovery phase involving those most familiar with specific regulatory frameworks is crucial. This ensures data science teams are able to translate their knowledge into rules that result in high-performing models for compliance.

The path to deeper insights

For every file sitting in a records management system, there will likely be data that relates to it within multiple databases. The ability to understand the link between each relevant piece of information across an organisation is not only useful from a compliance point of view, its essential for gaining a holistic view of your data universe.

This is where AI tools provide significant value for employees, as they make information discovery and deeper insights into that information seamless.

Reducing the cognitive load on users and improving employee experience is a key driver behind the uptake of AI tools and automation today. This is why the automation of governance is increasingly a valuable pay-off for organisations implementing more advanced data strategies, as employees no longer have to capture multiple datapoints for the sake of compliance as they go about their daily tasks.

The ultimate goal of any AI strategy, particularly when it comes to compliance, is to not only automate discovery and reporting, but to automate processes, compliance or otherwise, when new information is introduced to a system. To enable this model, advance your data strategy in line with the above recommendations and set yourself on a new path of data discovery.

Paul Maker is Chief Technology Officer, Aiimi. The Aiimi Insight Engine uses AI and machine learning to intuitively discover, enrich, and classify all information.