How Illusive Stops Ransomware
Attack Pathway Discovery and Elimination
Deny targeted ransomware access to sensitive assets by constantly identifying and removing attack pathways that could facilitate breaches.
Deterministically Detect Ransomware
Replace extraneous lateral movement paths with deceptive data that goads threat actors into revealing their presence early in the attack lifecycle.
Detour Ransomware Away from Crown Jewels
Deceptive data diverts ransomware away from mission-critical business data and prevents attacks from spreading to other hosts on the network.
Forensics to Speed Ransomware Response
Collect source-based forensics on in-progress ransomware attempts to prioritize alerts, coordinate remediation, and prevent future attacks.
Stop Ransomware with an Active Defense
Exponentially reduce ransomware risk by complementing probabilistic threat identification approaches with deterministic detection based on deception.
Reduce False Positives
Deterministic detection sets off alerts based on genuine attacker interactions – no guessing based on probability.
Respond to Ransomware in Real Time
Pinpoint and contain new ransomware threats before they can spread, encrypt devices at scale or cause systemic damage.
Detect Previously Unseen Threats
Illusive’s ransomware detection is based on the identification of malicious lateral movement and does not rely on signatures or pattern recognition.
Deploy MITRE Shield Active Defense
Leverage Illusive distributed deception to carry out the limited offensive actions outlined in MITRE Shield’s adversary engagement matrix.
ESG Analyst Explains
Watch video as Enterprise Security Group (ESG), Senior Principal Analyst Jon Oltsik, discusses how legacy anti ransomware solutions missed 2020’s biggest attacks, and how a strategy using deception technology confuses and detects ransomware attackers before they can encrypt machines at scale.
Schedule a Ransomware Risk Assessment
Proactively analyze your network and find the pathways ransomware attackers could use to encrypt devices at scale.