If you ask a cybersecurity professional what they like about their job, odds are high that somewhere near the top of the list is: “Things are always changing.” It’s almost a cliché that cyberthreats are always evolving, but it remains true. Threat actors are inventive, and many have the skills and motivation to continually refine their tactics.
Change applies to more than just tactics, techniques and procedures. Over the longer term, cyberdefense has trended away from a collection of distinct tools and technologies — each of which aims to provide protection against a particular type of attack — and morphed into a broader data problem.
Delivering effective cybersecurity today requires:
- Consuming a vast stream of telemetry and events from a wide range of signal sources
- Processing that data to identify attacks while avoiding false positives and negatives
- Equipping a team of expert analysts and threat hunters with the tools needed to investigate incidents and research advanced, evasive attacks
- Having the ability to continuously upgrade detection and defenses
These requirements demand changing the very technology foundations upon which cybersecurity solutions are built—moving from traditional security products and legacy MSSP services to modern cloud-native platforms.
Cloud-native is a Platform Imperative
While “cloud ready” means a solution can be deployed in the cloud, “cloud-native” goes much further. Cloud-native is about how a platform is developed, not about where it’s deployed. A cloud-native approach employs a service-based architecture in which processes and activities are self-contained and optimized to leverage the agility and flexibility afforded by the cloud itself.
A cloud-native platform offers important advantages over legacy approaches — advantages that provide real, important benefits for cybersecurity providers and the clients who depend on them. This new 451 Research study weighs in on what a best-of-breed, cloud-native cybersecurity platform should deliver in order to effectively stop cyberthreats in the digital age.
Differentiation that Makes a Real Difference
Building a cloud-native platform takes time, expertise and investment — it requires completely rearchitecting systems and overhauling software development methodologies. This level of commitment means there’s no quick catch-up option for providers who’ve been left behind with legacy architectures.
But there’s no doubt that the investment is worthwhile. At eSentire, we’re proud to be pioneers in delivering effective, efficient and scalable cybersecurity solutions; consistent with this track record, we started the long and challenging shift to cloud native years ago — and our clients are already enjoying the benefits of Atlas, our cloud-native platform.
Leveraging patented artificial intelligence (AI) technologies, Atlas learns across our global customer base and immediately extends protection to every customer with each specific detection. This ability to rapidly learn at cloud scale, combined with expert human actions, stops breaches and reduces customer risk in ways unattainable by legacy security products, traditional MSSPs and other Managed Detection and Response (MDR) providers.
Filtering Out the Noise
Finding the right security provider can be a real challenge, with many companies making the same claims. We see this every day amongst our competitive set from those claiming they provide MDR.
The right security provider hunts, identifies and contains attacks as they happen on your behalf, preventing breaches in real time. The wrong provider overwhelms your already-taxed security team with alerts, forces them to interpret the data and attempt to contain threats on their own.
A cloud-native platform is one of those differentiators. To help you make informed decisions about who to trust with your long-term cybersecurity needs, read Five Essential Questions to Ask Your Service Provider.
Dustin Rigg Hillard, Chief Technology Officer, eSentire
Dustin’s vision is founded on simplifying and accelerating the adoption of machine learning (ML) for new use cases. He is focused on automating security expertise and understanding normal network behavior through ML. He has deep ML experience in speech recognition, translation, natural language processing, and advertising and has published over 30 papers in these areas.
