The three pillars of modern business: Scalability, security and compliance

The three pillars of modern business: Scalability, security and compliance

Mark Molyneux, EMEA CTO at Cohesity, explains that achieving a balance between scalability and security in data management requires a holistic approach – integrating modern data protection technologies, robust governance frameworks, scalable infrastructure and stringent cybersecurity measures.

In the AI and digital age, an overwhelming amount of data is being generated, approximately 400 million terabytes each day, from a myriad of sources. Everything from social media interactions to professional communications, shipping timings to customer data – all this has to be stored somewhere.

Managing this data efficiently while ensuring compliance with an increasingly complex regulatory landscape is a significant challenge. However, by adopting a strategic approach that balances scalability and security, businesses can navigate this landscape effectively.

One of the foundational pillars of effective data management is data indexing and classification. Without a clear understanding of what data is being stored, why it’s being stored, its value and who may access it, it is impossible to manage storage efficiently. This can have significant knock-on effects such as difficulty to comply with regulations through an inability to provide accurate data for auditing, or the ability to leverage it for innovative opportunities, such as the recent advancements in AI.

Taking a step back always helps when it comes to creating an appropriate data strategy. Data indexing and classification provides clarity and control over stored data, which is crucial for maintaining strong data hygiene.

The value of data indexing and classification

When businesses index their data, they gain comprehensive knowledge about it, including details such as time, date, age, size and creator. Classification further enhances this by identifying what the data is and determining how long it needs to be retained based on the company’s relevant records policy. This approach has powerful implications for regulatory compliance, cost savings, AI insights, sustainability, cybersecurity and access control.

Cost savings are a significant benefit of data classification. By assigning data to the right storage tier, businesses can optimise both cost and access. By understanding what the data allows businesses to make defensible deletion decisions, so what to retain and what to remove. These prevent indiscriminate storage and reduce unnecessary data bloat, ultimately cutting storage costs. In an era where data storage can be costly, this is a crucial consideration for businesses looking to manage their budgets effectively.

Regulatory

From a regulatory compliance perspective, proper data classification ensures that data is retained for the appropriate length of time, helping businesses avoid legal risks. This is particularly important in light of regulations such as GDPR, NIS2, and most recently, DORA, which impose stringent requirements on data management and processing practices. By classifying data, businesses can ensure they meet these requirements and avoid hefty fines.

However, simply complying with each new data regulation as it emerges is not best practice nor is it strategic. This approach can lead to a reactive and fragmented compliance strategy, where businesses are constantly scrambling to meet the latest requirements. This not only increases the risk of non-compliance but also consumes significant resources and can disrupt business operations.

Instead, businesses should focus on creating a standardised process for data governance. A robust data governance framework provides a consistent and comprehensive approach to managing data across the organisation. This framework can serve as a template for regulatory compliance, ensuring that all data management practices are aligned with the highest standards and can be easily adapted to meet new regulations as they arise.

Artificial Intelligence

AI needs no introduction currently as it grips conversations at both a technical and economic level.  As businesses increasingly adopt AI, having a robust data management strategy becomes even more critical. Having Redundant, Obsolete and Trivial (ROT) data can often lead to hallucinations or to the sharing of private data in LLMs, potential reputational damage can follow if data is exposed, or inappropriately accessed.

Proper data classification and indexing are essential for effective AI systems. Organised and categorised data allows AI algorithms to identify patterns and relationships more easily, leading to precise predictions and recommendations. Indexed and classified data enables AI to access structured, relevant datasets, leading to more accurate and actionable insights. This can drive informed business decisions and enhance overall operational efficiency.

Effective data classification and indexing also maintain data integrity and security. By segregating sensitive information, businesses can implement access controls and encryption, reducing data breach risks and ensuring regulatory compliance. This is vital for AI, where misuse of personally identifiable information can have significant legal and ethical consequences.

Security

Ransomware campaigns and wiper attacks are common threats faced by cybersecurity professionals. In any successful breach, threat actors intend to take full command and control over corporate systems and associated backup data, and either ransom the data, or delete it outright to maximise damage.  Having proper data indexing and classification can improve access control, and help IT security teams truly understand the value of data that is ransomed or destroyed.

By restricting access to those personnel who need, or are authorised to see that data, businesses can minimise the risk of destructive data breaches permeating throughout the business and ensure that sensitive information remains secure. This is particularly important in an age where cyberthreats are becoming increasingly sophisticated. By moving towards a cyber-resilient posture business will be in a greater position to safely recover from a cyberevent without compromising themselves or their customers.

The path to strategic data governance

Implementing robust data governance frameworks is another critical step in balancing scalability and security. This includes establishing clear data privacy policies, conducting regular data audits, and training employees on data privacy and security best practices. By fostering a culture of data hygiene across all levels and business units, businesses can ensure that data is managed responsibly and securely.

By implementing a standardised data governance process, businesses can streamline their compliance efforts, reduce the risk of non-compliance, and ensure that they are always a step ahead of new regulatory requirements. This proactive approach not only enhances regulatory compliance but also improves overall data management, leading to better data quality, increased operational efficiency and greater trust from stakeholders.

Utilising scalable infrastructure, such as cloud solutions and hybrid models, can also help businesses balance scalability and security. Cloud solutions offer flexibility and scalability while ensuring robust security measures. Hybrid models, which combine on-premises and cloud solutions, provide a balanced approach that meets both scalability and security needs.

Enhancing cybersecurity measures is essential for protecting data. This includes using encryption to protect data at rest and in transit, implementing strict access controls, keeping systems and software up to date with the latest security patches, and the use of threat detection and hunting capabilities. By adopting these practices, businesses can safeguard their data against potential threats such as ransomware.

In conclusion, balancing scalability and security in data management requires a holistic approach that combines modern data protection technologies, robust governance frameworks, scalable infrastructure, and stringent cybersecurity measures. By leading with data indexing and classification, using scalable solutions, and fostering an organisation-wide culture of data hygiene, businesses can navigate the complex regulatory landscape while maximising the value of their data.