The ecosystem of information security is currently undergoing a paradigm shift. Historically, organizations have mainly focused on securing their infrastructure – firewalls, servers, and network perimeters – to safeguard sensitive and crucial data. However, this approach comes with its own limitations. Data breaches have now expanded beyond basic infrastructure. With the advent of emerging technologies, organizations now face higher security risks at multiple levels, and many of these concerns are difficult to navigate. Incidentally, ransomware attacks have increased by 27% in 2024, and yet majority of companies still do not have a formalized cyber plan.
On a brighter side, intelligence and data backed cyber security conventions are reflecting strong potential to prevent at least 80% to 90% of the emerging security threats. In fact, Data-Centric Security (DCS) has emerged as a revolutionary approach in addressing cyber concerns, providing a more proactive and holistic approach towards data protection.
Much needed rise in DCS
Data-Centric Security has transformed the traditional approach to cybersecurity. Instead of focusing solely on fortifying network perimeters, DCS prioritizes protecting the data itself throughout its entire lifecycle. Key elements of data-centric security include data classification, which involves identifying and categorizing data based on its level of sensitivity and importance, data protection, which involves implementing security measures that protect data from unauthorized access and breaches, and security tools, which utilize encryption, masking, and Data Loss Prevention (DLP) tools to ensure data integrity and confidentiality.
DCS playing a critical role in bridging gaps in information security
Implementing data-centric security is empowering organizations with granular control over data access and facilitates compliance with stringent data privacy regulations. This proactive approach is addressing critical gaps in information architecture including:
- The Behavior Gap: Balancing usability with security needs is challenging. Human error is a leading cause of data breaches, and data-centric security is helping mitigate this risk by making security measures more intuitive and less prone to error.
- The Visibility Gap: Traditional security measures often lose visibility of data once it is shared externally. Data-centric security is ensuring that data remains protected regardless of its location.
- The Control Gap: While internal controls can be tightened, leaked or lost information remains a significant risk. Data-centric security is preventing unauthorized access and data leaks.
- The Response Time Gap: The lag between recognizing new security risks and implementing countermeasures creates vulnerabilities. Data-centric security, enhanced by AI, can rapidly adapt to emerging threats.
Automated Data Classification, Predictive Analytics and Zero-Trust Approach is paving the way for cyber safeguarding
Beyond data, even artificial intelligence is augmenting safety. AI has enhanced data-centric security by identifying unusual activity patterns through anomaly detection, which may indicate a security breach and allow for prompt response and mitigation. It is providing context-aware access control by dynamically adjusting access controls based on factors such as the user’s behaviour, location, and device. AI is also amalgamating automatic data classification, automatically classifying and redacting sensitive data to ensure only authorized individuals have access. Additionally, user behaviour analysis (UBA) is powering AI to detect suspicious activity and potential insider threats. Organizations are leveraging AI to automate routine security tasks like incident reporting and data remediation, enhancing efficiency and response times.
Furthermore, companies have deployed AI threat intelligence and predictive analytics to analyse vast amounts of data, identifying emerging threats and predicting potential security incidents, while supporting a zero-trust approach by continuously verifying and validating all access requests.
The growing urgent need for Data-Centric Security in business management solutions (BMS)
As business management ecosystem has evolved drastically and deals with a growing number of connect devices, the potential surface for cyber threats has significantly increased. The rise of remote work and Bring Your Own Device (BYOD) policies, especially post-COVID, necessitates robust data protection measures. Ensuring data integrity is vital for maintaining uninterrupted operations and adhering to regulations such as GDPR, CCPA, and DPDP, which are essential for legal and reputational reasons. Protecting customer data is critical to build trust and enhance the organization’s reputation, while secure data enables more accurate and reliable decision-making processes. Additionally, AI-driven automation can optimize energy use and reduce CO2 emissions, addressing environmental and sustainability concerns. Finally, security measures should be user-friendly to prevent employees from seeking workarounds that could lead to data leaks, ensuring that data remains protected without compromising usability.
Hence, the shift from traditional infrastructure-centric security to Data-Centric Security (DCS) is crucial as it emphasizes protecting data throughout its lifecycle, acknowledging that breaches can occur despite robust network defences. DCS prioritizes data classification, protection, and tools like encryption and Data Loss Prevention (DLP), ensuring granular control over data access and compliance with privacy regulations. AI-driven DCS enhances security by enabling real-time threat detection, context-aware access control, automatic data classification, and predictive analytics. For the BMS industry, this approach is vital due to the growing number of connected devices, cyber threats, and the need for operational continuity and regulatory compliance. Small businesses, with limited resources, benefit significantly from automated and AI-driven security solutions that offer seamless integration and user-friendly measures, ensuring data protection without compromising usability. Embracing DCS with AI provides a comprehensive and resilient strategy to safeguard sensitive information in a digital world.
