Insiders are responsible for over 60% of attacks that stem from either malicious intent or an employee led unintentional action. Apvera Insight makes it easy for enterprises and small businesses alike to consistently watch over their environment for any suspicious activity from employees, contractors and outside vendors. Apvera offers context aware, which means to identifying employee behavioral patterns and reveal anomalous insider threats in an actionable manner reducing financial and data loss.
New Paradigm, New Risks
Insiders, usually employees, have an advantage when they threaten your data and privacy. Insiders already have access to the network, they understand security protocols, they have permissions to access sensitive data, and they have the ability to socially engineer greater security risk. No amount of perimeter defense is effective with insider threats given they are hard to identify. This leads to compromised valuable corporate and customer data exfiltrated from the corporate environment.
Since insiders already have the right credentials and privileges, there are rarely any proper monitoring and systems in place. Seemingly benign and legitimate actions are used to evade the system. Even the smartest traditional security tools are not normally able to pick up on these events. This leaves IP theft, financial fraud and other corporate crimes undetectable until it’s too late.
New Paradigm, New Approach
Employees are predictable as are their activities. Identifying deviances in behavior over a period of time or compared to peers within a group or the company as a whole can indicate fraudulent or malicious intent. Behavior patterns for users, devices and system accounts can be analyzed to reveal anomalies, even when they occur in very low frequency and over extended periods of time.
The Apvera Insight actionable user behavior analytics engine detects and captures the fingerprint of insider threats as they traverse across the enterprise network, private and public cloud, and mobile environments. Anomalies are arranged over time using advanced pattern detection and aggregation to reveal clear and immediate actionable events.