What you’ll learn:
Real life examples of how to create threat detection campaigns in a matter of minutes, which will automatically gather some of the most useful and relevant threat intel your team has ever seen.
Real life examples of how to create threat detection campaigns in a matter of minutes, which will automatically gather some of the most useful and relevant threat intel your team has ever seen.
How users of The Platform identify the most difficult-to-detect risks and mitigate them immediately, thanks to high-power automation and simplification.
How users of The Platform identify the most difficult-to-detect risks and mitigate them immediately, thanks to high-power automation and simplification.
How, just like a leading global bank client, your organization can eliminate the barrier to sophisticated detection and response operations.
How, just like a leading global bank client, your organization can eliminate the barrier to sophisticated detection and response operations.
Frequently Asked Questions (FAQ)
Detection and response refers to identifying malicious activity early and taking action to contain it before damage occurs. Effective detection focuses on finding real attacker behavior rather than inferred anomalies. Strong response capabilities ensure teams can act quickly, limit impact, and prevent recurrence.
CounterCraft improves detection by engaging attackers inside controlled environments that mirror real systems. When an adversary interacts with these assets, the activity is confirmed as malicious. This allows teams to detect threats earlier than tools that rely on signatures or baseline deviations.
Engagement-based detection generates alerts only when attackers interact with assets that have no legitimate operational purpose. Because these decoy systems are isolated from normal workflows, any interaction represents confirmed malicious activity rather than user error or policy violation. This eliminates the ambiguity that generates false positives in traditional detection tools.
CounterCraft captures detailed attacker behavior, including movement paths and actions taken during an intrusion. This context helps teams quickly assess scope, impact, and intent. As a result, response actions are faster and more precise, reducing both MTTD and MTTR. Find out how AI-powered deception works with a demo.
Yes. CounterCraft exposes lateral movement when attackers explore credentials, services, or systems within the environment. These actions trigger high-confidence detection that traditional perimeter or endpoint tools often miss. Teams gain visibility into attacker progression before critical assets are reached.
Yes. CounterCraft integrates with SIEM and SOAR platforms to feed verified attacker intelligence into existing workflows. This improves alert prioritization and response automation. Teams can enhance their current stack and adopt a preemptive cybersecurity stance without replacing established tools.
CounterCraft supports detection of ransomware activity, insider threats, credential misuse, APTs, and previously unknown attack techniques. It is one of the best tools for detecting lateral movement. Because detection is based on behavior, it remains effective even when attackers change tooling. This makes it suitable for both targeted and opportunistic threats.
SOC teams, incident response teams, and threat hunters benefit most from CounterCraft. It is especially valuable for organizations facing targeted attacks or operating complex environments. Teams gain earlier visibility and clearer decision-making support during incidents.
Reducing MTTR requires early detection and clear context about attacker behavior. When alerts are validated and actionable, teams spend less time investigating and more time responding. Engagement-based detection helps remove uncertainty from response decisions.
Passive defenses rely on observing traffic or endpoint behavior without interaction. Active defense exposes attacker intent by engaging adversaries directly. This provides earlier detection and clearer insight into how attacks unfold.
Accuracy improves when detection is based on confirmed attacker actions rather than assumptions. Reducing reliance on baselines and signatures helps eliminate false positives. Behavior-based detection provides stronger signals for prioritization and response. Find out how AI-powered deception works with a demo.