Skyhigh Networks: Skyhigh detects insider threats, privileged user threats, and compromised accounts leveraging machine learning. Skyhigh connects to Office 365 and immediately begins building behavior models based on actual user activity.
How Microsoft 365 Uses Machine Learning To Stop Data Leaks Insider Attacks
Take advantage of the Netskope advanced data loss protection (DLP) capabilities that provide contextual awareness of content being used in the cloud and include machine learning enhancements to simplify, expedite, and accurately scan and classify data. Display real-time notifications, including user coaching around risky activities and moving sensitive data.
Includes predefined regulatory and best practices compliance templates and supports technologies like file and binary fingerprinting, Optical Character Recognition (OCR), exact data matching, and machine learning enhanced classification.
Today, data is more available, more transferable, and more sensitive than ever. The best way to stop data leaks is to implement a data loss prevention (DLP) solution. DLP enforces an automated corporate policy, which can identify and protect data before it exits your organization
SpinOne integrates AI-based machine learning techniques in its activity anomaly detection services. The tool watches over access to sensitive data stores and builds up profiles of typical activity for each user account. This baseline of acceptable behavior provides a reference for the anomaly detection system. This enables SpinOne to identify insider threats and account takeover incidences.
Log360 is a unified SIEM solution with integrated DLP and CASB capabilities that detects, prioritizes, investigates, and responds to security threats. It combines threat intelligence, machine learning-based anomaly detection, and rule-based attack detection techniques to detect sophisticated attacks, and offers an incident management console for effectively remediating detected threats. Log360 provides holistic security visibility across on-premises, cloud, and hybrid networks with its intuitive and advanced security analytics and monitoring capabilities.
Uncover security threats accurately with Log360's various techniques such as event log correlation, threat feed analysis to identify malicious IPs and URLs, and machine learning combined with user behavior analysis to identify insider threats.
Its self-learning AI is modeled on the human immune system and used by over 4,000 organizations to protect against threats to the cloud, email, IoT, networks and industrial systems. This includes insider threat, industrial espionage, IoT compromises, zero-day malware, data loss, supply chain risk and long-term infrastructure vulnerabilities.
Insider threats do not exist in a vacuum, and organizations should address the risks associated with bad insiders along with a plethora of other cybersecurity risks concerning malicious software, Denial of Service attacks, ransomware targeting corporate machines, and any other threats.
There are some ambitious projects using machine learning. Deep Instinct is trying to use deep learning to map how malware behaves, so its appliances can detect attacks in real time, reliably enough to replace a firewall. More realistically, perhaps, Splunk is adding machine learning to its log analysis system to use behavioral analytics to detect attacks and breaches.
Securonix collects massive volumes of data in real time, detects advanced threats using innovative machine learning algorithms, enables you to quickly investigate the alerts that matter the most, and provides actionable security intelligence for an automated response.
Employees willingly and unwillingly expose or exfiltrate corporate data every day. Knowing the distinction between malicious intent and collaboration, followed by a response that is right-sized for the situation is the holy grail of Insider Risk Management. Our IRM framework provides the technical recommendations necessary to help analysts and architects machine their intuition in order to prevent data leaks, not collaboration.
User and Entity Behavior Analytics (UEBA) is a great tool against such attacks. After a learning period, it can pick up normal employee behavioral patterns and recognize suspicious activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.
Many cyber attacks target sensitive data and use techniques such as code injection, malware, or phishing to penetrate the security perimeter and gain access. In addition, attackers can target sensitive data via compromised privileged insider accounts.
More mature or advanced security measures for detecting irregular data access may also be appropriate for some organizations, including data integrity controls, honeypots, network traffic analyzers, security machine learning, and user identity checks or activity-based verification.
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