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What Machine Learning Means for Endpoint Security in 2020

June 1, 2020

By Peter Barnett

Cybersecurity threats are some of the most worrisome that are facing businesses today. The risk of a cybersecurity event that leads to data leaks or data loss is very real and the effects can be catastrophic. Attackers today are making use of often very sophisticated tools and attack techniques.

To counter these types of cybersecurity threats, your organization has to up the ante by using equally sophisticated and powerful tools to protect endpoints and detect new and emerging threats before they are able to compromise critical services and data.

To effectively win today’s cybersecurity war facing your organization, artificial intelligence (AI) and machine learning (ML) in endpoint security is a must. Let’s take a look at how AI and ML are being used in this space as an effective weapon against security compromise.

What are AI and Machine Learning?

Artificial intelligence (AI) and machine learning (ML) are common buzzwords that are thrown around in many spaces. In many software applications today, it seems that AI and ML are touted as features or capabilities. However, first, what exactly is artificial intelligence (AI) and machine learning (ML)?

These two terms seem like they may belong in a futuristic film or as part of science fiction. However, the technologies are very real tools for today’s modern applications. In fact, if you have interacted with the following technologies, you have actually already interacted with both AI and ML:

  • Apple’s Siri
  • Microsoft Cortana
  • Google Assistant
  • Targeted ads
  • Popular websites and services including
  • Facebook, Google, Twitter, Netflix, and many others

Artificial intelligence (AI) and machine learning (ML) are not competing technologies, but rather interwoven together. Artificial intelligence is the broader concept of the two technologies and is the concept of computers having the “intelligence” to solve problems on their own, without guidance to do so.

Machine learning (ML) is the more pinpointed of the two technologies and is the specific discipline of applying the characteristic of AI mentioned above putting into practice the ability of machines to make intelligent decisions based on the ingesting of data.

With ML, it uses advanced mathematical algorithms that parse and “learn” from data ingested. The data is filtered through by the algorithms to find patterns, anomalies, and points of interest that can be used in a meaningful way. Computers “learn” from the data in that decisions can be made from the data analysis for which the computers were not explicitly programmed.

This allows ML-enabled systems to perform tasks with little or no human intervention. As computers are exposed to more and more data that is effectively analyzed, they gain “experience” in a sense to make even more refined and accurate decisions.

With the mass of data in existence today and that can be gathered and stored from endpoints via logs and other monitoring, this can be fed into ML algorithms for powerful decision-making. This includes making decisions to protect against threats. Let’s see how this can be applied to endpoint security.

How AI Improves Endpoint Security

As mentioned in the outset, cybercriminals are making sophisticated attacks on today’s applications, data, and networks. Often, cybercriminals are using artificial intelligence and machine learning in carrying out attacks throughout all its phases.

This can involve using both AI and ML in information gathering, reconnaissance, finding vulnerabilities, and the actual attack itself. If the “bad guys” are using these new-age tools and “intelligence” that computers can provide, to successfully defend your environment, leveraging AI and ML can be a powerful ally.

Let’s put focus on the area of endpoint security. For decades now, organizations have struggled with traditional anti-virus and anti-malware applications to combat the plague of malware including the ominous ransomware infection, which can lock you out of literally all of your own data.

Against the new types of malware, including ransomware variants that can often go undetected by traditional endpoint security programs, artificial intelligence and machine learning can level the playing field against very hard to detect malware variants that traditional endpoint security applications miss. How can AI and ML help with endpoint security?

There are several key areas that AI and ML can help with your cybersecurity goals:

  • Detection
  • Incident response automation
  • Developing risk scores
  • Mobile device threat protection
  • More efficient footprint on endpoints

How AI and ML Are Helping in Critical Areas


Detection is the all-important area in which AI and ML are extremely powerful. Since they provide automated “intelligence” to your cybersecurity efforts, they can essentially provide the ability for your organization to have a sentient cybersecurity expert watching your endpoints at all times, 24×7×365.

Rather than being signature-based, AI and ML driven solutions work by developing a baseline of what “normal” activity and behavior looks like from an endpoint, applications, and users. The AI and ML algorithms essentially “learn” what the normal patterns and activities in the environment are comprised of. This makes it extremely easy to spot anomalies coming from an endpoint or malicious application behavior.

Having a person or multiple persons parsing through logs and watching monitors is extremely inefficient and not really feasible or effective in today’s world of complex infrastructure.

Incidence Response Automation

Machines are highly more efficient and faster than humans at performing automated actions across technology infrastructure. Today, automation is king of the hill with most organizations, no doubt including your own, moving to DevOps processes.

If a piece of malware infiltrates your environment on multiple PCs that are protected with AI and ML driven security solutions, AI and ML solutions can make split second decisions across your infrastructure. Using the “baseline” of normal activity, it can kill and blacklist malware processes that are found to be outside this baseline of normal activity.

Again, going back to the analogy of a security expert that is watching your environment 24×7×365, if the malware was activated in the middle of the night with no one around, it is effectively handled in an automated way by the “intelligent” decision-making of the AI-driven solution.

A key difference here is the solution did not have to wait for a new signature to come out to recognize the malware was abnormal in behavior. This provides far-superior protection against today’s very sophisticated malware variants that can mutate and bypass traditional detection and response.

Developing Risk Scores

The behavior analysis provided by AI and ML provides other benefits. It allows creating an associated “risk” baseline of application or known process behavior. Any application that is deemed “risky” can be globally defined so that these applications in particular are blocked before any type of activity is initiated.

Identifying the behavior and “intentions” of an application can help to prevent a malware attack before it even begins. If an application is known to connect back to domains that are associated with malware distribution or it attempts to make connections to unnecessary and risky ports (22, 23, 3389, etc), most likely it is up to no good.

Mobile Device Threat Protection

Employees today are using mobile devices more than ever before to carry out business-critical tasks. Mobile devices are very difficult to manage with traditional security solutions since they are generally connected to outside or remote networks for communication.

Many of the AI and ML endpoint protection solutions available are based in the cloud. This provides the ability to manage mobile devices wherever they are located or regardless of the network to which they are connected. No matter where the mobile device is located, your organization can still protect and manage these across a single-pane-of-glass interface and AI and ML driven technology.

Mobile devices connected to cloud SaaS environments can potentially expose your sensitive data and services to malicious third-party applications. AI and ML mobile device endpoint solutions can identity risky cloud applications and prevent these from connecting to your SaaS environment.

More Efficient Footprint on Endpoints

AI and ML cybersecurity solutions are able to much more efficiently cover most of the security tasks and initiatives required by most organizations. Whereas with traditional cybersecurity solutions, many organizations may have to run 2, 3, 5, or even more security agents to cover the range of threats, new-age AI and ML based solutions are providing the same functionality in one solution.

This provides a much more efficient footprint for endpoints that may have limited free resources available for use. It can help to eliminate helpdesk calls related to performance or other issues due to resource contention.

Corporate Endpoint Security Best Practices

Endpoint security and cybersecurity in general is like “layers of an onion” where all aspects work cohesively together to secure the environment. AI and ML cybersecurity solutions help your organization to effectively secure endpoints from threats and is an essential part of cybersecurity best practices including the following:

    • Use effective endpoint protection that is AI and ML based
    • Use role-based access control to effectively limit the scope of cybersecurity breaches
    • Use multi-factor authentication
    • Secure email and filter SPAM and phishing attempts

Provide end user training to recognize attacks


As infrastructure including endpoints are evolving to meet the demands of today’s businesses, cybersecurity threats have grown in complexity and sophistication. Traditional approaches to securing endpoints are no longer effective in the fight against attackers using new-age malware.

By using AI and ML endpoint security, your organization can effectively analyze the behavior of applications and processes so that malicious behavior can be pinpointed quickly and stopped before affecting business-critical data.

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