#SIEM – Xorlogics

Machine Learning and Data Protection in the Cloud

04 May 2020
by: Aisha Javed
in: BlogPost
Tags: #algorithms, #CLOUDCOMPUTING, #daatsecurity, #DATABREACG, #dataprotection, #ERP, #MultiCloud, #SIEM, Cloud, Gartner, MachineLearning
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Machine Learning and Data Protection in the Cloud

As more organizations are shifting infrastructure and services to the cloud, more are adopting a multi-cloud strategy. In fact theworldwide public cloud service market is expected to reach $623.3 billion by 2023. 83% of enterprise workloads will be in the cloud by 2020 and 94% of enterprises already use a cloud service.

These numbers show that most companies are already using cloud solutions in one form or another. In addition to the many advantages that the cloud can offer, it also increases the attack surface. Cloud growth creates new security threats, and organizations are struggling.

To guarantee a high level of security in the cloud, modern security solutions with machine learning, analytics and automation can help. Machine learning has already taken a massive increase in adoption over recent years and many businesses have already started to plan or have already developed machine learning models for security purpose.

Most IT departments are well equipped and informed with ensuring local network security. But how this really works in detail in the cloud is still relatively unfamiliar in many organizations. In addition, many business are already busy with securing their existing infrastructure, thus securing cloud infrastructures can be ignored or forgotten. What many businesses underestimate is that even complex hybrid infrastructures can be secured with advanced technologies that use machine learning, with a manageable cybersecurity budget.

In almost all industries, the cloud is already being used as an effective extension or replacement for some of the data center resources. And if you are not yet in the cloud, you are probably planning this step at the moment. But before moving to the cloud, make sure that critical security and regulatory questions are answered. As companies who adopt a multi-cloud strategy need visibility across it all to prevent downtime and to stay ahead of hackers.

Cloud-related threats are at an all-time high

It is not surprising that cloud-related threats keeps increasing, and there is no reason to believe that this will change in the near future. According to the recent study by the Ponemon Institute, the average cost of a data breach worldwide now equals $3.86 million – $148 per compromised record. However, the numbers vary greatly from country to country. In addition to that, a recent Skyhigh Networks study found that every organization operating in the cloud had almost two security incidents each month. These include: inside threats (both accidental and malicious), privileged user threats, compromised accounts, or data exfiltration. This shows that data is fundamentally endangered regardless of the storage location. Contrary to the often erroneous assumption, the provider of the cloud is never responsible for securing corporate data. For example, AWS states that they are responsible for the security of the cloud, but their customers for security within the cloud. Responsibility thus falls into the IT security of the companies using it, which often does not have the right personnel or know-how to guarantee security in the cloud.

Along with other challenges, IT security teams have a dual mandate to secure data in cloud services procured and managed by their organizations, as well as corporate data that employees upload to cloud services that they find and use on their own.

What specific steps can you take in this case to ensure security in the cloud as well?

A robust cloud security program includes technologies to protect, detect, investigate and respond to security incidents. The most effective cloud security technologies are data encryption, network encryption and SIEM (Security Information and Events Management). With the exception of SIEM, most technologies focus primarily on protecting and building a defense. However, next generation SIEMs also include proactive monitoring, analysis to improve detection and Reporting and forensics about security incidents, investigation and response to security incidents along with alerts based on analytics that match a certain rule set, indicating a security issue.

This makes the latest generation of SIEMs the ideal solution to secure hybrid environments. SIEM tools work by collecting and aggregating logs, reports and alerts from all of your security tools and solutions. These tools then present this information in a centralized location, by improving visibility and speeding incident analysis and response.

Machine learning key to securing data in the cloud

Fact that hackers are growing in sophistication and their malware is evolving quickly and continuously, enterprises must adapt a robust data protection strategy. Terms such as “data science” or “machine learning” started long ago as buzzwords in the IT industry. For some time now, however, the clever algorithms have been helping to recognize patterns in many areas and are already doing so in IT security. Many companies in numerous industries are currently facing the challenge of securing existing or future infrastructures in the cloud. Already used in speech recognition software and ERP systems for data management, the clever algorithms of machine learning are now being leveraged in cloud security solutions in order to enable enhanced threat detection and real-time security.

Next-generation machine learning algorithms are ideal for today’s cloud security because large data volumes are the most important condition for their reliability. These ‘intelligent’ solutions are able to take in high volumes of data from high-traffic environments andperform an extensive property and behaviour analysis to detect threats and take the appropriate actions in a variety of scenarios.

Therefore, the use of machine learning is the logical response to the growing amount of data and the ever-evolving threats found in the cloud. Machine learning solutions are helping organizations to secure data and enabling them to securely adopt any cloud application, despite advanced malware.

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Cyber Threat Intelligence – Know Your Enemy

17 June 2019
by: Aisha Javed
in: BlogPost
Tags: #CTI, #CyberThreat, #CyberThreatIntelligence, #SecurityInformationEventManagement, #SIEM, #ThreatIntelligencePlatform, #TIP
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Cybersecurity Ventures predicts cybercrime damages will cost the world $6 trillion annually by 2021, up from $3 trillion in 2015. It means that cybercriminal activity is one of the biggest challenges that individuals and companies will face in the next two decades.Therefore, it’s better to know about your potential business threats in order to face them. This is doable by investing in CTI, Cyber Threat Intelligence.

Cyber Threat Intelligence is to acknowledge threats by having the collection and analysis of information about current and potential attacks that threaten the safety of an organization or its assets. These include individual threats, exploits, malware, vulnerabilities or security breach metrics. Threat Intelligence is composed of many parts. It includes a variety of sources, feeds, and platforms that help businesses detect, analyze, and respond to threats. Together, these parts provide a wealth of valuable information about every aspect of cybersecurity.

Knowing about attacks is the foundation upon which all cyber security solutions are built. Without this knowledge, one cannot build an effective security solution and manufacturers are challenged to leverage the wealth of information CTI provides. The benefit of having threat intelligence is that it helps you preventing data breaches and saving you the financial costs of cleaning up after an incident.

Let’s see here below how an organization can protect against cyber threats, and how they use intelligence to augment and improve their security and business operations?

Data is The Foundation of CTI

Threat Intelligence’s only source is the raw data. This means gathering enough data to address the most important intelligence requirements. Data gathering can be done organically through different sources both internal and external. By leveraging threat intelligence from your own network, such as log files, alerts, and incident response reports, you can recognize and stop threats. If you use a Security Information Event Management protocol (SIEM), this is an ideal place to start. Several raw sources of internal network event data (such as event logs, DNS logs, firewall logs, etc.) are already present in your SIEM. Maintaining historic knowledge of past incident-response engagements is helpful in leveraging more mature threat awareness based on internal sources. External sources can be quite varied, with many degrees of fidelity and trustworthiness. “Open source” intelligence, such as security researcher or vendor blogs or publicly available reputation and block lists, can provide indicators for detection and context.

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Once the data is gathered, it needs to be stored and analyzed in order to feed CTI. A Threat Intelligence feed summarizes data from one or more sources. The majority of feeds tend to focus on a crucial area, such as Botnet activity, domains or malicious IP addresses. The real-time nature of Threat Intelligence feeds means that as soon as a new threat or malicious entity is discovered, the information is packaged in the feed format and streamed to subscribers so he can defend himself against impending attacks before they happen.

Threat Intelligence Platforms: The Essential Enterprise Software

To face the ever-increasing volume of cyberattacks, the adoption of a platform, that can support the entire security team and the threat-analyst teams, is highly required. In order to assist performing daily incident response, network defense, and threat analysis. The Threat Intelligence Platform (TIP) is used for operational day-to- day blocking and tackling, as well as strategic decision making and process improvement. It provides the ability to capture, organize, store, analyze, and compare multiple feeds simultaneously. You can then correlate these feeds with internal security events and create prioritized alerts that analysts can then review. In addition to many other applications, a SIEM also works this way. It should also facilitate the management of the Intelligence Lifecycle as it is used by intelligence organizations worldwide for a threat intelligence program.

Conclusion

Threat intelligence management is an ongoing effort. The threat landscape is already large, and it’s only growing, becoming more complex and getting more efficient as time passes. You have to constantly examine your defensive positions and adjust your operations and strategies to defend yourself against the evolving technologies and rivals that endanger your assets. In the same way that an individual pay for a gym and attends it regularly to keep fit, your organization must make a continual investment and commitment to protect your assets. Any delay is a moment of risk. Your assets are being examined. Your vulnerabilities are being identified.

Tag Archives: #SIEM

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