There are plenty of obvious questions you could ask each endpoint security vendor. But they don’t really help you understand the nuances of their approach, so we decided to distill the selection criteria down to a few key points. We will provide both the questions and the reasons behind them. Q1: Where do you draw the line between prevention and EDR? The clear trend is towards an integrated advanced endpoint protection capability addressing prevention, detection, response, and hunting. That said, it may not be the right answer for any specific organization, depending on the adversaries they face and the
Now let’s dig into some key EDR technologies which appear across all the use cases: detection, response, and hunting. Agent The agent is deployed to each monitored endpoint, so you be sensitive to its size and its performance hit on devices. A main complaint regarding older endpoint protection was performance impact on devices. The smaller the better, and the less performance impact the better (duh!), but just as important is agent deployability and maintainability. Full capture versus metadata: There are differing strong opinions on how much telemetry to capture and store from each device. Similar to the question of
The next set of key Endpoint Detection and Response (EDR) capabilities we will discuss is focused on response and hunting. Response Response begins after the attack has happened. Basically, Pandora’s Box is open and an active adversary is on your endpoints, probably stealing your stuff. So you need to understand the depth of the attack, and to focus on containment and returning the environment to a known safe state as quickly as possible. Understand that detection and response are considered different use cases when evaluating endpoint security vendors, but you aren’t really going to buy detection without buying
As we resume posting Endpoint Detection and Response (D/R) selection criteria, let’s start with a focus on the Detection use case. Before we get too far into capabilities, we should clear up some semantics about the word ‘detection’. Referring back to our timeline in Prevention Selection Criteria, detection takes place during execution. You could make the case that detection of malicious activity is what triggers blocking, and so a pre-requisite to attack prevention – without detection, how could you know what to prevent?. But that’s too confusing. For simplicity let’s just say prevention means blocking an attack
As we continue documenting what you need to know to understand Endpoint Advanced Protection offerings, it’s time to delve into Detection and Response. Remember that before you are ready to pick anything, you need to understand the problem you are trying to solve. Detecting all endpoint attacks within microseconds and without false positives isn’t really achievable. You need to determine the key use cases most important to you, and make an honest assessment of your team and adversaries. Why is this introspection necessary? Nobody ever says they don’t want to detect active attacks and hunt for adversaries.
There are plenty of obvious questions you could ask an endpoint security vendor. But most won’t really help you understand the nuances of their approach, so we decided to distill the selection criteria down to a couple of key points. We’ll provide not just the questions, but the rationale behind them. Q1 If your prevention capabilities rely on machine learning, how and how often are your machine learning models retrained? An explanation here should provide some perspective on the vendor’s approach to math and the ‘half-life’ of their models, which indicates how quickly they believe malware attack
As the velocity of technology infrastructure change continues to increase, it is putting serious stress on Security Operations (SecOps). This has forced security folks to face the fact that operations has never really been our forte. That’s a bit harsh, but denial never helps address serious problems. The case is fairly strong that most organizations are pretty bad at security operations. How many high-profile breaches could have been avoided if one of many alerts was acted upon? How many attacks were made possible by not having properly patched servers or infrastructure? How many successful compromises resulted from human error?
After exploring prevention approaches, you should understand some common technologies which are foundational to endpoint advanced prevention offerings. Machine Learning Machine learning is a catch-all term to indicate that the endpoint protection vendor uses sophisticated mathematical analysis on a large set of data to generate models for detecting malicious files or activity on devices. There are a couple mathematical algorithms which can improve malware prevention. Static file analysis: With upwards of a billion malicious file samples in circulation, mathematical analysis of malware can pinpoint commonalities across malicious files. With a model of what malware looks like, advanced prevention products then
Every year, as I travel the security conference circuit, hallway conversations always turn to, “See anything interesting?”. To be honest, I can’t remember the last time I was excited about an honestly cool security technology (which I didn’t create myself, but let’s not go there today). I see plenty of cloud innovation, and plenty of security evolution, but not a lot of revolution. A week ago I picked up my iPhone X. Although I received a background brief on Face ID a couple weeks earlier, I hadn’t gotten my hands on it until then. And, really,
Let’s resume our discussion of endpoint attack prevention approaches with the options available once an attack actually begins to execute, or once it has already executed on a device. During Execution (Runtime) Once malicious code begin to execute, prevention of compromise requires recognizing bad behavior and blocking it before the attack can take control of the device. The first decision point is whether you want the protection to run in user mode (within the operating system and leveraging operating system protections) or kernel mode (at a lower level on the device, with access to everything – including interactions between the