Organizations are now facing an increasing set of cyberattacks from various places. Cybercriminals don’t just cost a business hard-earned cash, but they can also ruin its reputation, causing distrust in their existing, past, and potential customers.
Cybersecurity experts are constantly trying to keep up with attackers and the changing landscape of our new reality. Meanwhile, attackers continue to target large organizations with remote workers housed in insufficiently secure environments.
Despite sophisticated cybersecurity tools and solutions, cyber experts now know that attacks still happen. It is no more a matter of ‘if’ but a question of ‘when’.
Moreover, they usually go undetected for a long time, during which a cybercriminal does all the damage they can and gain further access into the infrastructure.
Machine learning-led algorithms and artificial intelligence somewhat remedy the situation, but these systems need data as the fuel to power their efficiency and speed.
That brings us to question the criticality of available data in order to uncover attacks faster and minimize damage.
Internet of Things as a technology used in households as well as businesses has increased the available surface for cyberattacks, resulting in a more urgent need for their rapid detection and mitigation.
As stored information grows in volume as well as value, an organization becomes increasingly alluring to attackers. The very data that attracts attackers can become a way of powering cyber threat analysis systems. By using big data collected from computers, networks, sensors, and cloud infrastructure, intrusion detection and prevention systems can work in real-time.
The resultant data can be used to detect system vulnerabilities and attacks that are becoming prevalent to tailor security systems to an immediate need.
Big data and analytics can easily justify its role in ensuring cyber resilience because of the need to process high velocity and volume data from disparate sources to discover anomalies and limit system vulnerabilities.
Cyberattack detection capabilities need to identify changes in use patterns. That is possible by executing complex analyses in close to real-time. This essentially would allow performing complex correlations across multiple data sources ranging from application and server logs to user activities and network events.
In order to achieve this, you might need advanced data analytics beyond a rules-based approach and the ability to analyze huge amounts of current as well as historical data. Combining insights from the current state with security will help organizations improve their cybersecurity posture.
Enhanced by contextual data and threat intelligence, this data can be analyzed using correlation algorithms to identify anomalies and potential threat activities.
Even though many tools have been developed supporting the use of data analytics in cybersecurity, their use warrants new approaches considering many aspects. This includes zero-day attack detection, data sharing across detection systems, unified data representation, real-time analysis, and so on.
You might be wondering, why does this make sense now more than ever?
The big data analytics industry has finally reached a point where business intelligence algorithms have become commoditized and are not affordable only to large corporations with big cybersecurity budgets.
This concludes that data and analytics are not only feasible but a necessary solution to improving cybersecurity for various organizations. Traditionally, security systems have been focusing on the perimeters of IT infrastructure and some have now started focusing on servers and other endpoints.
Instead of simply looking to collect data, companies are now focused on collecting the right data and analyzing it to gain insight into communication and services across the environment. This can even help security experts uncover stealth attackers that launch sophisticated attacks.
Data is the lifeblood of any organization today, and it can now power the engine that moves cybersecurity beyond generating and responding to alerts to signaling actionable insights that augment decision making.
Data-driven cybersecurity is imperative to safeguarding all of an organization’s assets. An outside-in approach to cybersecurity from the POV of hackers can help organizations empower their security teams with granular analytics capabilities and holistic visibility of network and system vulnerabilities.
This will also allow organizations to monitor the cybersecurity posture of third-party vendors, uncover predictive breach capabilities, and prioritize areas that organizations can focus on to meet regulatory compliance and standards requirements.
Data-driven cybersecurity efficiency depends on the approach that organizations take. Here are a few best practices to follow:
The scale of making a data-driven change with your cybersecurity posture can seem overwhelming, but the TEHTRIS XDR Platform can help in such a situation. Start by making easy and small changes and then incrementally building on top of them.
Having accurate and up-to-date data means that your organization can stop security from becoming a blocker to your progress by becoming more agile and relevant and bringing security to the digital revolution.
Data-driven security is the future, and we can help you get started. Should you be among the ones already understanding where the future is heading, please contact TEHTRIS and together we can build your perfect infrastructure solution.