Corporate risk management now includes cybersecurity for its network, data, and employees. The amount of cybersecurity attacks and threats continue to increase and their impact to business operations are becoming more damaging. The impact is not only affecting the security posture and internal technology operations of a business, but also affecting customers’ trust and availability of applications required by the business to conduct operations. Corporations are now faced with addressing and managing cybersecurity risks and vulnerabilities daily as a top priority from their board of directors and C-suite leaders. To combat threats to the enterprise, security organizations are implementing predictive analytics as a core method to understand the systems and user behaviors within their networks and to gather information and data on the external traffic patterns which can identify anomalies and outliers potentially identifying an attack or vulnerability. The more data analyzed, the better a security operations team can prepare to reduce the risk of negative business outcomes or operational downtime attributed to security breaches.
Predictive analytics is the application of machine learning techniques to large collections of information and data from various sources to identify patterns of behavior and reliably predict an outcome or scenario. In cybersecurity predictive analytics is used to safeguard and implement proactive response measures to protect critical business data, operations, and applications. Security operations personnel are utilizing the data received from monitoring systems to develop usage patterns and behaviors which predict the expected actions inside and outside a network to detect potential threats and vulnerabilities. The actions can be from bad actors external to a company attempting to gain access through an exploit or weakness, or it can also be from internal employees that are attempting to access systems and data not typically associated with their daily job role. Either can be a threat to business operations and result in operational downtime if the action or threat is not mitigated. As threats spread through a corporate network, they can affect resources vertically and horizontally to further exploit the vulnerability or weakness. During this progression, business functions and applications are interrupted, data can be compromised, and the value of the company is threatened. This is extremely damaging and the cost to mitigate and remediate these threats, along with restoring customer trust, can cost millions in investigations, labor, legal action and restoring customer’s service.
Implementing predictive analytics within security operations organizations provides critical visibility to understanding the risks threatening a company and its network. Through learning the internal and external behaviors of the corporation, companies will have a distinct knowledge of expected patterns of actions across their network. Anomalies in these patterns can be indicative of threats to the corporate network, users, and your critical business information. It allows security organizations to focus on prioritizing, investigating, and mitigating these threats and attacks identified as outliers. Predictive analytics is a crucial component of security operations organizations to change the culture of combatting threats from a reactive posture to proactive awareness, investigation and mitigation. See our Preparing through Prediction paper for more information on this topic.