Traditional security approaches fail at dealing with all this new data. Vulnerability points are too numerous to monitor manually. Traditional rules-based cybersecurity worked well against known attacks and attackers, but today’s attacks and attackers are often unknown before they are uncovered at-work within a system. And human-led detection is too slow to keep up with today’s barrage of AI-driven cyberattacks.
To contend with your new data-flooded threat landscape, you must evolve past traditional security approaches, and begin to deploy your own big-data-driven, AI defenses.
The right AI platform enhances every level of your cybersecurity system. It increases the speed and accuracy of your prediction, monitoring, detection, and response. To do so, your platform collects and processes a staggering volume of raw data in search of the tell-tale anomalies of an attack.
Every attack—even an unknown attack—leaves a network event trail. Properly uncovered and analyzed, these anomalies show you the steps an attacker has taken within your network. They can tell you how the attacker breached your systems, where they have been, where they are likely going, and what their plausible aim might be. Uncovering and analyzing this network event trail essentially turns an unknown attack into a known attack—one you can effectively respond to, and one you can prevent in the future.
However, to uncover and analyze this network event trail, you must collect, analyze, contextualize and process every piece of raw data produced by your network. Modern cyberattacks both approach through a wide variety of vulnerability points, and, once they breach your perimeter, exhibit many different behaviors as they move through many files, networks, protocols, and systems to reach their target. As such, you cannot ignore any piece of data that moves through your network’s flows, forensics, and logs. An anomaly can appear anywhere. Any anomaly can indicate a breach. And only a big-data-driven platform can process the volume of data required to find them, analyze them, and raise the red flag.
It’s easy to oversell the power and use of multichannel big-data in cybersecurity. But the approach—while necessary—can’t do everything. At the end of the above-mentioned data collection, analysis, and processing, your platform still needs to bring a focused list of anomalies to your human security staff. They are still required to investigate and potentially act on this data. (And, of course, human cybersecurity experts are also still required to program the big-data platform to ask the right questions in the first place.)
But, even the best big-data cybersecurity platform is not one-size-fits-all. No single security analytics system can detect modern, blended attack vectors on its own. Every big-data-driven security system must evaluate multiple dimensions at once, and in correlation with each other. (Paladion’s system combines all four modern security analytics methods—Endpoint, User Behavior, Network, and Application Threat Analytics in a single platform to detect sophisticated attacks.) In addition, every security system must be able to evaluate the nine types of modern attacks, and their combined use (they are Advanced Malware, Social Engineering, Lateral Movement, Insider Threats, Transaction Frauds, Account Takeovers, Data Exfiltration, Run-Time App Exploits, and Encrypted Attacks). And even with this platform, each organization must define the specific use cases they require to meet their unique security needs.
Simply bringing some analytics to your organization is not enough. A security analytics based on a comprehensive data-based system, supported by human insight, and fine-tuned to your specific needs, can protect you from today’s evolved threat landscape.