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Security analytics is an approach to cybersecurity that, like SIEM (Security Information and Event Management), analyzes data to detect anomalies, unusual user behavior, and other cyber threats. It aggregates data from across the entire ecosystem and turns that data into actionable insights so that IT can proactively act to minimize risks and prevent security incidents. Advanced network security features like artificial intelligence (AI) and machine learning (ML) further help by automating the detection and remediation process.
This can offer faster and more comprehensive protection from security events without complicating the employee experience. In addition to external threat intelligence, a sophisticated security analytics solution provides proactive visibility across an organization, improves the user experience, and ultimately drives better business outcomes.
A security analytics solution should be able to monitor IT performance across an organization’s architecture as well as analyze behavior data for potential threats. For an analytics platform to be effective, it must provide critical security data regarding user activity, a network traffic analysis, and anomaly detection. The three main performance areas that an IT security solution should be able to report on include network, applications, and device performance.
If performance is poor in any of these areas, there is a greater likelihood that malware will slip past threat detection solutions and work undetected in the infrastructure. By using a security analytics tool equipped with AI and ML, along with security policies and best practices, organizations can make big strides towards reducing risks across their architecture.
Data breaches exposed
records in the first six months of 20193
Machine learning is a software capability that allows software to improve its own performance at a particular task using relevant data. With 69 percent of IT leaders saying that AI and machine learning is transforming their business1, it’s little surprise that the most advanced security analytics solutions integrate machine learning. In contrast to the predefined and fixed data transformations that many security analytics solutions include upon installation, ML-capable security analytics transform their own performance and capabilities by being adaptive and responsive to big data. Here’s how this works:
This unsupervised anomaly detection is one of the most common and important ways that machine learning works with security analytics. Outside of security, machine learning can also continually analyze performance data to quickly identify issues and pinpoint their root causes.
Hackers attack every 39 seconds, or an average of 2,244 times a day2. With cyberattacks and breaches continuing to rise, cybersecurity is a top business concern for today’s C-suite. Whether through malicious activity, insider threats, or unintentional leaks, organizations suffer as a result of lost data. Negative repercussions can include loss of revenue or brand reputation, expensive lawsuits, massive governance and compliance fines from violating regulations like HIPAA and GDPR, and disruptions to operations. Breaches can wreak havoc for IT teams as well. Considering the average time to identify a breach is 206 days3, just becoming aware of a security issue is time consuming. Remediation after a breach also uses valuable personnel hours and eats into budget intended for other purposes.
This in mind, the primary benefit of security analytics is delivering end-to-end security visibility to IT. This shows IT the current state of their security across geographical information, access and logins, SaaS and Web App use, virtual apps and desktops events, data, and endpoints. To prevent damaging security incidents, a strong analytics platform should proactively address attempted breaches by finding and flagging abnormal user activity using behavior analytics, and then instantly respond instead of react. This provides security assurance to IT and business leaders that they know the existing state of their security posture and how to improve it going forward.
Insider threat via a company’s own employees (and contractors and vendors) is one of the largest unsolved issues in cybersecurity.2
McKinsey & Company
One of the top needs of security analytics is a holistic approach that examines . Because 34 percent of data breaches involve internal actors4, behavior analytics can help recognize security threats from internal users before they turn into costly data breaches. In addition, a secure workplace is crucial to detecting anomalies and potential cyber threats. It also allows employees access to all necessary apps while ensuring data security from the inside out.
A best-in-class security analytics solution is automated to examine all data, traffic, and activity across the entire infrastructure. By monitoring and applying machine learning to user behavior, security analytics solutions can better identify unusual activity and quickly provide security alerts. This end-to-end view enables IT to take a proactive approach to security instead of a reactive one.