In a data-driven world, organizations possess vast amounts of data, since data is produced at an accelerated speed; as files are created, saved and shared, leading to the creation of terabytes or even petabytes of data. The challenge becomes apparent when its time to pinpoint sensitive data in millions of files, in unstructured and structured […]
In a data-driven world, organizations possess vast amounts of data, since data is produced at an accelerated speed; as files are created, saved and shared, leading to the creation of terabytes or even petabytes of data. The challenge becomes apparent when its time to pinpoint sensitive data in millions of files, in unstructured and structured data; which in most cases is an impossible endeavor. With regulations enforcing strict data processing practices on companies that handle personal data, the demand for innovative monitoring and auditing tools, as well as sedulous security assessments, has never been higher. It also creates a need for the modification of existing data classification models and algorithms, in order to comply with the introduced data security standards.
There is, however, a light at the end of the regulatory tunnel. By utilizing robust data governance solutions that incorporate machine learning and the analytical powers of data science, organizations can get a deeper understanding of the information they possess and aid in easing compliance. With the insights obtained from these solutions in hand and regulatory compliance at bay, organizations can start making data-driven decisions that will elevate their business to the next level.
Classification is a critical step towards securing data because it keeps the focus on the data that matters the most. Classification algorithms come in handy when the desired output is a distinct label. Not only does it aid in meeting regulatory requirements and overall governance, but also significantly simplifies the process for internal stakeholders to search for and retrieve data. Computing power has become available to train bigger and more complex models much quicker. Graphics Processing Units (GPUs) that were initially designed to render video game graphics have now been repurposed to effectuate the data crunching needed for machine learning. This compute capacity has further been aggregated into hyper-scalable data centers that can be accessed via the cloud. Machine learning algorithms consume enormous amounts of data and support superlative complexity and variability in the data. More importantly, they are more adaptable to changing data points and parameters.
Leveraging the powers of machine learning (ML) to classify content makes it possible to easily identify similar data sets and group them together for faster retrieval and search. When ML is coupled with natural language processing (NLP), data inputs like metadata, documents, instant messaging, emails, or even spoken word can be accurately interpreted. This can be taken a step further by developing an algorithm that matches specific regulatory requirements, thus simplifying the process entirely. A robust, well trained, AI-based classification engine is posed to locate Personally Identifiable Information (PII) across an organization’s entire data landscape and trigger actions that help enterprises delete or retain data – both challenging yet critical aspects of the GDPR. Regulated data typically comprises structured data with a consistent pattern. Training a machine learning algorithm with patterns for recognition of medical records, social security, and credit card numbers, and other forms of PII together with policies for HIPAA, GDPR and other regulations from around the globe accelerates compliance readiness. Add in features like automatic identification of risky keywords and the end result is enriched classified content that can be searched with precision and ease.
Manual processes are typically inconsistent, arduous, and unenforceable. However, by utilizing modern AI based classification technologies, organizations can make sensitive data easier to locate, and redundant data easier to delete.
Simply keeping an eye out for fraudulent practices no longer cuts it. Organizations have to monitor communications for ‘intent’ as well. This can only be accomplished by obtaining additional context relating to monitored users and their respective activities – which may include behavioral anomalies or other fluctuations in communications data. The increasing complexities in regulatory requirements are driving the expectations for such detailed analysis. The current approach is to throw more bodies at the issue, but this is expensive and simply not scalable – especially as the enterprise grows and expands into new regions, which introduces more regulatory pressure. Artificial intelligence powered by big data and machine learning has the ability to wholly revolutionize the compliance industry. Applying AI and automation activities foster productivity growth and other benefits for compliance teams.
Machine learning technologies can only enhance the compliance process if they fit into the organization’s current workflow. AI can’t operate in a silo in the compliance world (at least not yet). However, ML enhances the compliance team’s view of monitored users, help identify compliance related communications, and if properly implemented, facilitate analyst decision making. A compliance team benefits in the following ways.
1. Significantly lowered costs – compliance in itself is a costly venture. ML will result in large cost reductions as a result of more accurate analysis of big data.
2. Coherent regulatory compliance – the coherence comes as a direct result of real-time risk detection abilities as well as the digitization and automation of manual compliance and reporting processes.
3. Lower risk of fraud – with an exhaustive focus on monitoring all communication channels, including speech, which has been especially vulnerable to fraudulent activity, instances of fraud are easily identified and minimized.
4. Information de-duplication – clusters of similar or duplicate content are easily identified, thus cutting through the noise and reducing the amount of content that needs review.
5. Unlocking the value in content – compliance professionals can rely on machine learning to help them make sense of the large amount of data they encounter on a daily basis, including updates and regulatory changes.
Regulatory requirements such as the GDPR will challenge an organizations’ existing data governance cultures and processes. However, with a sound approach to data management that is fueled by machine learning and data science, this anxiety-filled, time-consuming procedure can not only lead to more feasible compliance but analytics insights that foster strategic decision-making.
Author: Gabriel Lando
