AI is revolutionising data classification and regulatory reporting by introducing more dynamic, accurate, and efficient practises that will change the way we work forever. Traditional, static approaches to data management are making way for models that offer enhanced contextual understanding and rapid adaptability. Organisations that embrace these changes can expect to improve data management, enhance decision-making, and streamline regulatory compliance.
According to Gartner: By 2026, 75% of businesses will use generative AI to create synthetic customer data, a significant increase from less than 5% in 2023.
If you want to stay at the front of the pack when it comes to data classification and compliance, this month weâre going to explore the transformative role of AI. Weâll look AI driven classification, its impact on regulatory reporting, and what the future holds for organisations willing to embrace these innovations.
Introduction to AI and Data Classification
Traditionally, data classification has been a manual process, relying heavily on predefined categories and human judgment. But humans get things wrong, and as data volumes grow and become more complex, traditional methods are increasingly falling short of the mark. AI brings a new dimension to data management by offering the ability to automatically classify data with greater accuracy and efficiency, enabling you to react quickly to new information and evolving contexts.
But this doesnât mean the role of humans will become obsolete. We were lucky enough to interview AI expert, Dr. Stefan Oglesby recently, who told us:
“Probably the biggest misconception is overestimating the capabilities of AI. AI will not automatically deliver valid results without prior human input. AI models require proper training regarding their specific task. The output of AI models also needs continuous human oversight to ensure accuracy of results. A further potential misconception is that AI can replace human judgment. In general, AI can augment human decision-making, not replace it. Domain expertise is crucial. Consequently, the challenge is how to optimize efficiency with a maximum level of automation, whilst ensuring human-level quality control and input.”
Impact of AI on Traditional Data Classification
Like so many things in the world of technology these days, AI is fundamentally changing the way data is managed. Traditional data classification often falls short because it relies on static categories that donât account for the dynamic nature of modern data. AI algorithms, on the other hand, can analyse data in real-time, identify patterns, and adjust classifications as new data emerges. Using AI, organisations will be better positioned to handle diverse datasets, ensure more accurate data management, and derive more meaningful insights.
AI Algorithms and Contextual Analysis
One of the key things AI algorithms are really good at is contextual analysis, allowing them to interpret data in ways that traditional methods canât. For example, while a conventional system might categorise customer feedback solely based on sentiment (positive, negative, neutral), AI can identify subcategories like product-specific comments and keywords, suggestions for product improvements, or even frequent complaints, and create new categories as needed. This nuanced understanding is achieved through machine learning techniques that draw on vast datasets and recognise subtle patterns that might be overlooked by human analysts.
In the financial sector AI is also being used to great effect by classifying transactions not just by type (credit, debit, transfer) but also by patterns that suggest potential fraud, investment trends, or customer preferences. Similarly, in healthcare, AI can classify patient data not just by disease type but also by genetic markers, lifestyle factors, and treatment responses, leading to more personalised and effective care.
But AI is not a mythical silver bullet against burgeoning data management needs, as Dr. Stefan Oglesby warns:
âAs with most real-world applications of (gen) AI, the quality and availability of data is the key challenge. Disparate, unstructured, often siloed data requires significant cleansing and integration prior to further processing with AI. Thus, any organisations starting the journey of using AI for classification, needs a robust data governance and appropriate ETL processes.
Often, skill gaps and misjudging the required change management are significant barriers. Cross-functional teams need to be established. Ongoing training must be provided, to achieve the necessary upskilling.â
Benefits of AI-Driven Classifications
The new classifications enabled by AI offer several benefits. Firstly, they enhance the accuracy and relevance of data. By understanding data in context, AI systems can reduce errors and improve the granularity of insights. This leads to better decision-making, as organisations can rely on more precise data analytics.
AI can also help identify and eliminate âshadow dataâ, which 68% of security professionals see as their primary challenge in 2024. Dr. Stefan Oglesby explained:
âAI has the potential of uncovering âshadow dataâ, i.e. untracked, unclassified data, by systematically scanning and categorizing all data across the organisation. This significantly enhances detection and visibility of so far âshadowedâ data. AI, if specifically trained and fine-tuned, will classify data in a more systematic way, reducing risks and improving data governance.â
Secondly, AI-driven classifications contribute to operational efficiency. Automated classification reduces the need for manual data handling, which allows staff to focus on more strategic tasks.
Enhancing Regulatory Reporting with AI
Challenges in Regulatory Reporting
Regulatory reporting is a critical function for many organisations, particularly in industries such as finance, healthcare, and energy. However, it is also fraught with challenges. The sheer volume of data can make it hard to manage the complexity of regulatory requirements, and the extra workload can lead to inaccuracies and even late- reporting.
The numbers donât look good as close to70% of respondents in an ISC report said they believe their organisation doesnât have enough cybersecurity staff to handle cloud data risk effectively.
And as the world changes, so does the regulatory framework. Traditional data classifications can be too rigid or simplistic to adapt quickly, failing to capture the nuances required for comprehensive regulatory compliance in this modern age.
AI Solutions for Regulatory Reporting
By automating the classification and analysis of data, AI can help organisations more accurately and efficiently meet regulatory requirements. AI tools, such as natural language processing (NLP) and machine learning models, can sift through vast amounts of unstructured data, identify relevant information, and classify it according to specific regulatory needs. For example, AI can automatically categorise financial transactions based on compliance criteria, identify discrepancies, and flag potential issues for further review.
We asked Dr. Stefan Oglesby to speculate on what regulatory innovations might come in the future:
âLooking ahead, I foresee a major breakthrough in AI for data classification and compliance involving the development of self-regulating AI systems. These systems would not only classify data but also ensure compliance with evolving global regulations autonomously. This advancement would drastically reduce human error and oversight costs, making compliance seamless and more efficient, which is crucial as data privacy and protection laws become increasingly stringent.â
Future Outlook
The future of AI in regulatory reporting looks promising, and this is just the start. Emerging trends and technologies are rolling out at a rapid rate, such as advanced NLP algorithms and deep learning techniques, which will allow even more precise data analysis and classification. In the future, the integration of AI with blockchain technology could provide more secure and transparent regulatory reporting processes, which will increase the potential applications for this technology exponentially.
With a keen interest in consumer behaviour, Dr. Stefan Oglesby sees another innovation on the horizon that all organisations with a large customer base should be excited about:
âI strongly believe, that the benefits of AI go far beyond costs and efficacy. Things can be done with AI that could not be done before! With a âchat your dataâ approach, the decision maker can use the original, anonymized consumer conversations to answer questions at any time, and to support ideation processes. For example, a product manager can use an AI tool to create innovative product, and services concepts based on the authentic, unfiltered voice of customer.â
If youâre interested in exploring how AI can transform your data classification and regulatory reporting practices, now is the time to act. Start by evaluating your current data management strategies and consider integrating AI tools to unlock new opportunities for growth and compliance. Dr. Stefan Oglesby has this advice:
“As any data related journey, you should start with strategy, as Bernard Marr puts it, i.e. specify the business problem AI is expected to actually address. The output is as good as the input, therefore a proper data preparation process is crucial. The adequate infrastructure, i.e. tools, platforms, and teams, must be carefully selected, balancing costs and time. Finally, a ‘pilot and iterate’ approach will avoid the risk of high costs and delays. Starting with a small, focused pilot project is highly recommended, followed by iteration based on the results.”
By leveraging AI, you can not only keep pace with the rapidly changing data landscape but also gain a competitive edge over your competitors.
The Importance of GRC Tools in AI-Driven Data Management
As AI continues to reshape data classification and regulatory reporting, the role of robust Governance, Risk, and Compliance (GRC) tools becomes even more critical. GRC frameworks provide the foundational structure needed for clear data management and access controls, ensuring that data is accurate, secure, and compliant with evolving regulations. This foundation is essential when integrating AI into your data management strategy, as it helps mitigate risks associated with data breaches, privacy concerns, and regulatory non-compliance. By prioritizing GRC tools, organisations not only protect themselves from potential liabilities but also create a solid base upon which AI can deliver its full potential, driving innovation and maintaining a competitive edge in an increasingly data-driven world.