Machine Learning and Artificial Intelligence. Two terms which Information Security relies on, in order to face many of the current challenges. Recent reports prove that a high number of security professionals are convinced that Machine Learning enhances their ability to prioritize threats and vulnerabilities and increases the productivity of security personnel.

The challenges today for cybersecurity have a broad range, from the intelligence and threat research phase to the Incident Response. Many security professionals are struggling from insufficient resources to keep current on new threats and vulnerabilities. Also, when they set their defending mechanisms, it’s difficult not το get overwhelmed by the vast number of alerts when the Security Incident and Event Management (SIEM) mechanism is not tuned and optimized. And finally, when they get to the Incident Response phase, a major challenge is to reduce the average incident response and resolution time.

Let’s focus on Incident Response. Incident Response (IR) or Incident Handling (IH) or Incident Management has become necessary for organizations and institutions today because attacks frequently cause the compromise and leak of personal and business data. Incidents involving malicious code continuously disrupt and damage millions of systems and networks around the world every year. And apart from business reasons to establish an incident response capability, organizations must be compliant with relevant laws, regulations, and policies, related to defense against information security threats.

According to NIST, Incident Response is The mitigation of violations of security policies and recommended practices”. To effectively implement this capability, an organization should have an incident response plan. The plan describes a high-level approach for how the incident response capability fits into each organization.


Which are the challenges today?

The biggest challenge today for Incident Responders worldwide is how to minimize the reaction time for Incident Handling in order to reduce the consequences of the incident and improve the effectiveness of the plan. But as we mentioned above, incident detection and analysis are not easy because frequently indicators are not accurate and ideally every indicator should be evaluated to determine if it is legitimate, making this process significantly difficult.

Also, the Incident Response process faces one more issue, because the number of indicators may be thousands or millions a day, and digging in order to find the real security incidents is always a challenging task. Even if an indicator is accurate, it does not necessarily mean that an incident has occurred because the cause of the issue could be a human error or a misconfiguration. 

For all the above the best approach usually is to build a team of highly experienced members (the Incident Responders) who can analyze the precursors and indicators effectively and efficiently and take appropriate actions, following a pre-defined process and documenting each step taken. But today these people are missing from the industry. Here comes Machine Learning to the rescue! 

But what is Machine Learning and which are the use cases related to information security? Machine Learning is when the computer “learns” something about the data and is able to take decisions by itself. Use cases involve AI techniques that can help security professionals recognize patterns in the vast amount of log file data that our machines are producing today. And they can also do this task in a fraction of time compared to humans! This feature of Machine Learning is extremely useful for Incident Response because it reduces the response times and helps mitigate the issue as soon as possible! 


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The problem with Artificial Intelligence and its implementation was its maturity. Professionals were not convinced that this technology actually helps during operations because in its first steps it used to take awkward decisions which led to cause more problems rather than solve them. Today, most believe that it is mature enough to effectively help analysts during all phases of operations. Specifically for Incident Response, AI has already proven its value to speed up the identification and incident classification.

Logstail platform is integrating Artificial Intelligence in order to proactively alarm you about issues occurring in your network. This feature is called Insights and offers Anomaly Detection to provide granular insights from high-volume log streams by identifying and isolating anomalies. You can save time and resources by identifying the problem in less time and mitigating it before it gets a real issue for your infrastructure.

Our cloud-hosted solution with all these advanced features brings the functionality of centralized monitoring to your hands. Convert your data into actionable insights and maximize the performance of your infrastructure or be notified of potential problems and take the appropriate actions. Sign-up for a free demo in order to realize the power of Logstail!


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