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SWORDFISH INCIDENT MANAGEMENT MODULE

The SWORDFISH® Incident Management module is designed to provide operationalization and automation of handling cyber security incidents, with the objective of minimizing damage and/or exposure resulting from a security breach. Its purpose is to accelerate incident management through a well-structured, focused, and coordinated approach, while also reducing the mean time to response.
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RESPONSE PROCESS ADHERES TO INDUSTRY STANDARDS

The module is fully aligned with ISO/IEC 27035 practices and requirements, ensuring that the response process adheres to industry standards.

By utilizing this platform, organizations can effectively manage cyber security incidents and protect sensitive and critical information.

CAPABILITIES

As part of its core functionality, provides the following capabilities:

  • Implement A Structure and Consistent Framework for the coordination, tracking, response and monitoring of all cyber security-related events and cyber security incidents

  • Enable Structured and Coordinated Response Capabilities during the incident lifecycle to minimize and/or eliminate the impact

  • Serve As A Conduit for developing and distributing cyber security incident related communications, including awareness activities

  • Implement Policies and standards for incident management

  • Provide Executive Level Updates that enable management to make timely and informed decisions

  • Enforce A Methodology that allows management to envision the greater threat scenario (and required response actions to that scenario)

  • Document and Track All Cyber Security Incidents reported to the team to allow for trending and incident analysis and correlation

  • Minimize Response and Resolution Times by enabling predefined playbooks and response scenarios

  • Incident Analysis in Real-Time with comparable categories root-causes and keywords across locations and units to give actionable insights that help you work safely and efficiently

  • Benchmarking with Peers on regional and international levels based on anonymized data