As the amount of data being generated and stored by organizations continues to grow, data gravity centers are facing unique challenges in cybersecurity. Today a leading provider of software development services, STX Next is tackling these challenges.

If you are wondering what the latest trends and developments in the cybersecurity field are, as well as the concept of “data gravity centers”, the risks and inefficiencies involved in using them, you’re in the right place. On STX Next’s latest Tech Leaders Hub session we interviewed Avi Chesla, founder & CEO of Cybersecurity Stealth-Mode Startup. As a cybersecurity expert with over 20 years of experience in tech, Avi has seen firsthand the rapid evolution of the industry. 

TREND 1: Vendor Consolidation

Cybersecurity remains a top priority for organizations as the threat landscape continues to evolve and expand. One of the main challenges faced by organizations is the proliferation of security tools, leading to a fragmentation of knowledge and a need for more and more specialized personnel. This has given rise to a trend of vendor consolidation, which has been ongoing for over 7 to 8 years. The market has become less accommodating for point solutions, which add yet another layer of complexity for organizations to navigate.

In response to these challenges, organizations are seeking out cybersecurity platforms, such as Extended Detection and Response (XDR) solutions. Development often has an appetite for cybersecurity platforms (like XDR, next generation) which allow you take your existing tools without purchasing anything new and consolidate all those different tools into 1 language. This not only simplifies the management of security systems but also ensures that SOC teams are able to communicate effectively with all of their security tools, allowing them to respond to threats more quickly and efficiently.

Leading tech companies, including Google, Microsoft, and Amazon Web Services, have invested heavily in cybersecurity platforms to meet the needs of organizations. These platforms are designed to collect and analyze data from a range of security tools, including endpoint security tools, cybersecurity cloud services, and firewalls, classifying it into a unified language of cyber attacks. This enables organizations to quickly detect and respond to suspicious activities, ensuring that they are always one step ahead of potential threats.

TREND 2:  Cybersecurity Mesh Architecture (CSMA)

The second major trend in cybersecurity is the Cybersecurity Mesh Architecture (CSMA). This innovative approach to cybersecurity is centered on identity and seeks to build a collaborative and integrated ecosystem of security tools and controls to secure modern, distributed enterprises.

The CSMA model takes into account the increasingly complex and dynamic threat landscape, with a focus on integrating composable security tools and centralizing the data and control plane to achieve more effective collaboration between these tools. The outcome of this approach is a more robust and adaptive security system, with enhanced detection capabilities, improved response times, consistent policy management, and granular access control.

This architecture is based on the principle of identifying high-priority identities and monitoring their activities closely. By collecting detailed information on the behavior and anomalies of these identities, organizations can better understand the potential threats they face and take proactive measures to mitigate them.

For example, identities with access to confidential data are considered high-priority as they can have significant impact on the business if compromised. The CSMA concept helps organizations to focus their security efforts on these key identities and protect their valuable assets. 

TREND 3: Attack prediction

Cybersecurity has always been a rapidly evolving field, and one of the most pressing challenges has been to reduce the time taken to detect and respond to attacks. As technology continues to advance, the capability to predict these attacks is becoming increasingly important. “Everybody is trying to reduce the time to detect and respond to attacks. One of the most demanding abilities in the cybersecurity area is to predict such attacks. In recent years the development of algorithms and different methods to predict behavior have grown significantly, also in the field of cybersecurity”, says  Avi Chesla. The development of algorithms and sophisticated behavioral analysis techniques have given cybersecurity professionals the ability to not only understand what is happening in real-time, but also to anticipate the next steps of an attack. “Based on the data that cybersecurity tools collect all the time you can analyze it and understand what’s happening right now, but also you can develop capabilities to predict next steps of attacks”, adds Chesla.

This predictive power is a game-changer for the industry, allowing organizations to be proactive in their defense rather than reactive. For example, if a hacker manages to steal the identity of one of a company's employees, predictive analysis can anticipate that the information may be shared outside of the organization. With this information, the company can take preventative measures, such as disabling certain permissions for the compromised identity, to prevent further manipulation of sensitive data.

TREND 4: Counter attacks

The concept of counter-attacks in cybersecurity is a hotly debated topic, with many organizations and individuals hesitant to take a proactive approach to defending themselves against cyber threats. However, in 2023, the trend is shifting towards a more assertive stance on counter-attacks. The idea is that if an organization or individual is under attack, they should be able to respond in kind, rather than simply playing defense.

The logic behind this approach is that by taking a counter-attack, organizations can raise the cost for the attacker and deter them from continuing their attack. This is particularly effective when the counter-attack is designed to disrupt the attacker's operations, such as forcing their computer to reboot. By taking a proactive approach to defense, organizations can make themselves a less attractive target, thereby reducing their overall risk.

In the ever-evolving world of digital data, organizations are tasked with finding the best locations to store and manage their vast amounts of information. This is where the concept of Data Gravity Centers comes into play. The idea behind it is that as a significant amount of digital data accumulates in one location, additional services and applications will be drawn to it due to the inherent latency and throughput requirements.

Data gravity refers to the irresistible pull of large datasets that attract smaller datasets, relevant services, and applications. This is because larger datasets offer a diverse range of information, making them desirable, and the technologies used to store such large amounts of data, such as cloud services, offer various configurations for processing and utilizing data.

Data gravity centers present a cybersecurity challenge for organizations as they often opt for aggregating all their data in one central place, such as the cloud. However, this approach can be costly, and creates the problem of data gravity.

Data gravity presents two critical challenges to data managers: latency and non-portability. Latency occurs when a large dataset requires applications that use it to be physically close, leading to a reduction in workload performance. To combat this, the enterprise must ensure that throughput and workload balance grows alongside the increasing size of the dataset, which can only be achieved by moving applications closer to the data.

On the other hand, non-portability is a result of the sheer size of the dataset, making it increasingly difficult to move. The enterprise must take into account the growing size of the dataset when developing migration plans and consider the likelihood of attracting additional services, applications, and data. Migrating vast quantities of data is both slow and resource-intensive, requiring a specialized and creative approach.

Examples of data gravity in action include applications and datasets moving closer to a central data store, either on-premise or co-located. This approach maximizes the use of existing bandwidth and reduces latency, but also limits flexibility and makes it challenging to scale in response to new datasets or applications.

In conclusion, data gravity centers pose a significant challenge to organizations, requiring a strategic and well-thought-out approach to managing and migrating digital data.

Data gravity centers, where an organization aggregates a significant amount of its digital data, present various challenges including increased latency and costs. However, a proven solution to overcome these challenges is to adopt the "Shift Left" concept.

This concept involves conducting a thorough classification of the data, determining which data holds high value and which does not. By scoring the data as it is generated from the source, organizations can make informed decisions about whether to shift the data to the left, indicating that it does not hold enough value to justify being sent to the data gravity center, or to the right, meaning it does hold significant value and should be included in the aggregation.

Adopting this approach results in the creation of smaller datasets, reducing the costs associated with storage, compute, and network operations, as well as minimizing latency. This approach not only streamlines operations, but it also enables organizations to maintain agility, flexibility, and scalability in their data management practices.