Asaf Kochan, President & Co-Founder of Sentra, offering data security for the AI era.
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The cybersecurity landscape is undergoing a significant transformation, marked by new entrants and a surge in acquisitions of Data Security Posture Management (DSPM) startups by major tech vendors—six deals in just the past 13 months. Meanwhile, organizations are often playing catch-up with their security strategy, as the nature of risks and emerging technologies are rapidly evolving. With data breaches soaring nearly 500% in the first half of 2024 and the growing demand to protect the data entered into AI models and algorithms, there’s an urgent market need for innovative, tailored data security solutions.
While large vendors’ all-in-one solutions address some needs, they often lack the flexibility to fully meet the unique and nuanced requirements of different organizations. That’s because when it comes to security, there are four main layers—identity, data, application and infrastructure—and they all require very different solutions with varying degrees of granularity and classification. This further enforces why an all-in-one solution cannot handle everything well; instead, organizations often prefer solid integrations between multiple best-of-breed solutions that are connected through one cohesive process. when done right, these types of integrations address the tool sprawl issues that consolidation attempts to solve. The spotlight on data security is calling attention to what makes data so valuable and challenging to protect, why it’s being breached and how organizations can keep pace with their security posture.
Data Is The Most Valuable Asset
Data is the most valuable asset for organizations, making it appealing to threat actors—some of whom have been able to cause many of the most damaging breaches in recent history in this year alone on companies including AT&T, Change Healthcare and Ticketmaster. As the volume of valuable data grows, so does the sensitivity and risk of the information being created, particularly as it is moved, copied and accessed. From financial records to personal health data, organizations handle various sensitive data types that require tailored protection from unauthorized access and exploitation. Simultaneously, the proliferation of the cloud has created more complexity, as organizations now operate in multi-cloud environments and are tasked with protecting the data that exists in them. The vast and interconnected nature of the cloud and the increasing number of tools in organizations’ tech stacks widen the potential attack surface. Advanced data security solutions provide the necessary capabilities to organize and protect large amounts of data in any and all locations where it exists.
Breaches Will Inevitably Occur
Organizations face an overwhelming challenge: not only to defend against cyber threats but to proactively ensure that their data remains secure as cyberattacks are deemed imminent. In addition, new regulations are becoming more stringent as governments and federal agencies implement standards to hold organizations accountable for data breaches. As new regulations emerge and existing ones—like GDPR and HIPAA—evolve, organizations must continuously update their data security practices to avoid legal repercussions and maintain compliance. AI has also brought a host of new compliance concerns. The passing of the EU’s AI Act will require full transparency into data used for training models, and data processing activities must comply with privacy laws and control risks of data poisoning. Comprehensive data security measures must be implemented to achieve effective risk management and governance to meet these regulatory requirements and protect their data assets. Organizations that fail to secure their data not only face compliance violations but also risk damaging their reputation and losing trust.
Organizations Can’t Protect What They Can’t See
Shadow data, whether created intentionally or not, is data that lacks adequate security protections, making it vulnerable to exploitation. As enterprises increasingly migrate to the cloud, the concern over shadow data grows, bringing with it compliance and security risks. This unprotected data also leads to unnecessary cloud storage costs, impacting the bottom line. A new IBM study found "a third (35%) of breaches last year featured shadow data, leading to a 16% increase in breach costs. These incidents took 26% longer on average to identify and 20% longer on average to contain." To address these issues, data classification processes can ensure time and resources are spent protecting only the most critical, sensitive data. Sensitivity varies by organization—what’s sensitive to a financial services company may differ from a healthcare company. Effective data security measures that include data classification can not only identify "hidden" data but also understand the data type and prioritize the remediation of the largest risks.
Adopting Data Security Strategies
Building a data security strategy involves having a full understanding of a data landscape by discovering and classifying all data. Effective data discovery and classification are fundamental for understanding an organization’s data environment, assessing risk, implementing controls, monitoring and detecting threats, offering incident remediation and ensuring compliance.
This process can be technically complex, and doing it manually is nearly impossible, given the amount of data that is created each day, moved and duplicated across an organization’s data estate. Organizations can adopt technology such as DSPM and DDR tools to automate this process and can quickly gain full visibility into the sensitivity of their data, what is at risk and how to proactively protect and remediate vulnerabilities. Leaders should also look to keep their employees up to date with the most recent best user practices in cybersecurity.
The type, amount and compliance requirements for data in each industry are highly varied. By adopting data security solutions and uptraining their employees, organizations can achieve a level of customization with how their data is organized and protected—particularly important in sectors with unique security challenges—and one-size-fits-all solutions may be insufficient.
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