Security Orchestration, Automation, and Response (SOAR)

• Data Silos and Integration Complexity: SOAR’s effectiveness heavily relies on integrating data from diverse security tools – SIEMs, firewalls, endpoint detection and response (EDR), threat intelligence platforms, and more. Currently, many of these tools operate with disparate data models, APIs with varying levels of maturity, and differing levels of data quality. This creates significant technical hurdles in establishing seamless data flows, real-time correlation, and a unified view of security events, requiring substantial investment in middleware, custom connectors, and ongoing maintenance.
• Lack of Standardized Event Formats: The absence of universally adopted event standards (like STIX or Cybersmile) continues to plague SOAR deployments. While initiatives exist
• Complex Playbook Development & Maintenance: Creating and maintaining effective SOAR playbooks – automated workflows designed to address specific threats – is a significant challenge. These require deep understanding of security operations, threat landscapes, and individual tool capabilities. Poorly designed or overly complex playbooks can introduce new vulnerabilities, slow down response times, or even trigger false positives. Maintaining playbook accuracy as threat landscapes evolve requires continuous monitoring, testing, and updates, a resource-intensive task.
• Orchestration of Human-in-the-Loop Actions: While SOAR excels at automating repetitive tasks
• Skill Gap in SOAR Implementation & Management: There’s a global shortage of skilled professionals who understand both security operations and the nuances of SOAR platforms. Organizations struggle to find individuals capable of designing
• Measuring & Demonstrating ROI: Quantifying the value of SOAR investments can be difficult. Metrics beyond simple reduction in incident response time are needed. Demonstrating the impact on reducing analyst burnout
• Dynamic Threat Landscape Adaptation: SOAR automation relies on pre-defined rules and playbooks. However, the constantly evolving nature of cyber threats means that these configurations quickly become outdated. Adapting SOAR to new attack vectors and malware families requires ongoing model retraining, dynamic playbook updates, and potentially significant adjustments to threat intelligence feeds—a task that demands continuous vigilance and sophisticated analytical capabilities.” }

Basic Mechanical Assistance (Currently widespread)

• **Security Information and Event Management (SIEM) Rule-Based Correlation:** Automating alerts based on pre-defined signature matches and simple log correlation events (e.g., multiple failed login attempts from the same IP address triggering a notification).
• **Basic Vulnerability Scanning Automation:** Scheduled scans utilizing tools like Nessus or Qualys automatically generating reports based on pre-configured rules and thresholds. The outputs are then manually reviewed and acted upon.
• **Initial Virus Signature Matching & Containment:** SIEM rules automatically identifying and blocking known malware signatures, followed by a manual investigation of the affected endpoint.
• **Ticket Routing & Triage Automation:** Automating the initial routing of security tickets based on the reported incident type – e.g., all phishing emails are automatically sent to the email security team.
• **Automated Log Collection & Forwarding:** Centralized log management systems automatically collecting logs from firewalls, intrusion detection systems (IDS), and servers, forwarding them to the SIEM.
• **Network Threat Intelligence Feed Integration (Basic):** Automatically updating threat intelligence feeds within SIEM and initiating basic blocklists based on matched IP addresses or domains.

Integrated Semi-Automation (Currently in transition) (Currently in transition)

• **SOAR Platform Integration with SIEM:** Utilizing a SOAR platform (e.g., Swimlane, Demisto) to trigger workflows based on SIEM alerts. This can include initiating enrichment requests (e.g., looking up an IP address's geolocation), isolating endpoints, and sending automated notifications.
• **Automated Endpoint Isolation with Threat Intelligence:** Integrating threat intelligence feeds directly into endpoint detection and response (EDR) solutions to automatically isolate endpoints exhibiting suspicious behavior based on enriched threat data.
• **Automated Response to Phishing Campaigns:** SOAR platform integration with email security gateways to automatically block known malicious domains, quarantine emails, and trigger user awareness training based on detected phishing attempts.
• **Automated Vulnerability Remediation (Basic):** Utilizing SOAR to automate patching for critical vulnerabilities identified through vulnerability scans, triggered automatically based on severity and patch availability.
• **Automated Workflow Orchestration across Security Tools:** Defining workflows to execute a sequence of security actions across different tools (e.g., triggering a firewall rule, updating threat intelligence, and initiating a forensic investigation) based on predefined criteria.
• **Integration with Threat Intelligence Platforms (TIPs):** Automatically updating TIP data and using the enriched data for proactive threat hunting and prioritizing investigations.

Advanced Automation Systems (Emerging technology) (Emerging technology)

• **AI-Powered Threat Hunting:** Utilizing ML algorithms within a SOAR platform to analyze security data, identify anomalies, and proactively hunt for hidden threats based on behavioral patterns.
• **Dynamic Risk Scoring & Prioritization:** ML-driven risk scoring systems continuously analyzing data from various sources (endpoint, network, cloud) to dynamically prioritize security alerts and automate investigations based on real-time risk assessments.
• **Automated Incident Investigation & Analysis:** Employing Natural Language Processing (NLP) within SOAR platforms to automatically analyze security alerts, investigate incident details, and generate comprehensive reports.
• **Automated Deception Technology Integration:** Using SOAR to control and manage deception technology (honey pots, honeynets) to attract and analyze attacker activity and then automatically initiate containment procedures.
• **Self-Healing Security Posture:** SOAR integrating with cloud security posture management (CSPM) tools to automatically remediate security misconfigurations and vulnerabilities in cloud environments.
• **Behavioral Analytics for User and Entity Behavior (UEBA) Automation:** UEBA systems utilizing machine learning to establish baselines of normal behavior for users and entities, automatically detecting and responding to deviations indicative of malicious activity.

Full End-to-End Automation (Future development) (Future development)

• **Autonomous Incident Response:** A fully automated incident response system capable of independently analyzing, investigating, containing, and recovering from security incidents without human intervention – including dynamically adjusting defenses based on evolving threat landscape.
• **Predictive Security Analytics:** AI-powered systems predicting potential attacks before they occur based on vast datasets, vulnerability trends, and threat actor behaviors – enabling proactive mitigation.
• **Dynamic Policy Enforcement:** SOAR platforms automatically generating and enforcing security policies in real-time based on continuously analyzed threat data, automatically adapting to new vulnerabilities and attack patterns.
• **Digital Twins for Security:** Creating digital replicas of the organization’s IT infrastructure for simulated attack scenarios and automated defense testing - driving continuous improvement and proactive vulnerability discovery.
• **Blockchain-Based Security Automation:** Utilizing blockchain technology to ensure tamper-proof logging, automated policy updates, and secure collaboration among security teams.
• **Composable Security Architecture with Microservices:** A fully modular and adaptable security architecture allowing security teams to quickly assemble and deploy custom automation workflows based on specific needs and emerging threats.

Small scale

• Timeframe: 1-2 years
• Initial Investment: USD 20,000 - USD 50,000
• Annual Savings: USD 8,000 - USD 20,000
• Key Considerations:
  • Focus on automating repetitive tasks within existing SOC workflows (e.g., ticket triage, basic alert enrichment).
  • Utilizing readily available, off-the-shelf security orchestration platforms with a simpler UI.
  • Integration with existing SIEM and ticketing systems is crucial; careful planning is key.
  • Limited scalability – ROI primarily driven by efficiency gains within a single team.
  • Smaller teams may require significant training and change management efforts.

Medium scale

• Timeframe: 3-5 years
• Initial Investment: USD 100,000 - USD 300,000
• Annual Savings: USD 40,000 - USD 150,000
• Key Considerations:
  • Expanding automation scope to include complex incident response scenarios (e.g., containment, eradication).
  • Requires a more robust SOAR platform with advanced analytics and integration capabilities.
  • Integration with a wider range of security tools and data sources.
  • Increased need for skilled personnel to manage and maintain the SOAR platform and automated workflows.
  • Requires a phased implementation approach with clear success metrics.

Large scale

• Timeframe: 5-10 years
• Initial Investment: USD 500,000 - USD 2,000,000+
• Annual Savings: USD 150,000 - USD 500,000+
• Key Considerations:
  • Full automation of incident response across multiple teams and security domains.
  • Requires a highly customizable and scalable SOAR platform with advanced orchestration capabilities.
  • Integration with a vast ecosystem of security tools and data sources, often including cloud-based services.
  • Significant investment in skilled personnel (orchestrators, analysts, developers).
  • Demand for continuous platform optimization and adaptation to evolving threats.
  • Requires rigorous testing and validation of automated workflows.

Key Benefits

• Reduced Mean Time To Detect (MTTD)
• Reduced Mean Time To Respond (MTTR)
• Improved Security Analyst Productivity
• Enhanced Threat Visibility
• Compliance Automation
• Scalable Security Operations

Barriers

• High Initial Investment Costs
• Integration Complexity
• Lack of Skilled Personnel
• Resistance to Change
• Platform Lock-In
• Inadequate Testing and Validation
• Poorly Defined Automation Requirements

Recommendation

Large-scale deployments offer the highest potential ROI due to their ability to automate complex, organization-wide security operations, particularly when integrated with a robust, scalable platform and experienced personnel. However, the initial investment is significantly higher, and careful planning is paramount.

Sensory Systems

• Advanced Computer Vision Systems (ACVS): Multi-camera systems employing deep learning for real-time threat detection, anomaly detection, and forensic analysis. Incorporates thermal imaging, multispectral imaging, and 3D reconstruction for enhanced situational awareness. Capable of identifying and classifying objects, people, and behaviors in dynamic environments.
• Acoustic Threat Detection Systems: AI-powered systems analyzing audio data for gunshot detection, suspicious noises, and unusual patterns. Utilizes microphone arrays and advanced signal processing for precise localization and classification.
• Sensor Fusion Platforms: Real-time data integration from multiple sensors (ACVS, acoustic, vibration, environmental) via standardized communication protocols. Uses Kalman filtering and Bayesian networks for optimal data interpretation and predictive analytics.
• Network Intrusion Detection Systems (NIDS) – Autonomous: NIDS enhanced with AI to proactively identify and respond to emerging threats by analyzing network traffic patterns in real-time.

Control Systems

• Robotic Response Units (RRUs): Mobile robots equipped with manipulators, sensors, and communication capabilities for physical intervention, evidence collection, and system access. Capable of navigating complex environments and executing pre-defined tasks autonomously.
• Drone Swarms – Security Applications: Coordinated groups of drones utilized for surveillance, perimeter security, and rapid response to incidents. Includes autonomous navigation, cooperative control, and payload delivery capabilities.
• Cyber-Physical System (CPS) Control Interface: A standardized, secure interface allowing direct control of physical security systems (access control, CCTV, locks) from the automated response platform.

Mechanical Systems

• Modular Robotic Arms: Reconfigurable robotic arms with interchangeable end-effectors for diverse tasks (evidence handling, door manipulation, system access). Utilizing force sensors and tactile feedback for precise control.
• Smart Locks and Access Control Systems – Autonomous Override: Access control systems integrated with robotic control, allowing for remote lock manipulation and authorized entry/exit.
• Miniaturized Intervention Tools: Small, highly dexterous tools for physical intervention, capable of operating in confined spaces.

Software Integration

• AI Orchestration Platform (AOP): Centralized platform integrating and managing all automated security processes, including threat detection, response planning, and post-incident analysis. Utilizes machine learning for adaptive response and continuous improvement.
• Digital Twins – Security Environments: Virtual representations of physical security environments, allowing for simulation, testing, and predictive analysis. Used for training and optimizing automated responses.
• Blockchain-Based Audit Trails: Immutable record of all security events and actions, ensuring accountability and facilitating forensic investigations.

Performance Metrics

• Orchestration Latency (Average): ≤ 50ms - The average time taken for the SOAR platform to initiate and execute an orchestrated workflow. Measured from event receipt to workflow activation.
• Automation Success Rate: ≥ 95% - The percentage of automated workflows that successfully complete without manual intervention. Factors in workflow design, data quality, and system integration.
• Incident Resolution Time (Average): ≤ 15 minutes - The average time taken to resolve a security incident using the SOAR platform. Includes detection, investigation, and remediation stages.
• Workflow Throughput (Events/Second): ≥ 500 - The number of security events the platform can process and manage concurrently. Dependent on event volume and complexity.
• API Response Time: ≤ 100ms - The response time of APIs used by the SOAR platform for integration with other security tools.
• Scalability (Concurrent Users): ≥ 100 - The number of simultaneous users who can access and utilize the SOAR platform without performance degradation.

Implementation Requirements

• SIEM Integration: - Seamless ingestion of security alerts and events from the SIEM.
• SOE Integration: - Enable correlation of data between the SOAR platform and the SIEM.
• Threat Intelligence Feed Integration: - Real-time threat intelligence data to enhance detection and response.
• Playbook Management: - Predefined workflows for standardized response actions.
• Role-Based Access Control (RBAC): - Security and compliance requirements.
• Reporting and Analytics: - Performance monitoring and operational insights.
• API Gateway: - Secure API management and control.

• Scale considerations: Some approaches work better for large-scale production, while others are more suitable for specialized applications
• Resource constraints: Different methods optimize for different resources (time, computing power, energy)
• Quality objectives: Approaches vary in their emphasis on safety, efficiency, adaptability, and reliability
• Automation potential: Some approaches are more easily adapted to full automation than others

By voting for approaches you find most effective, you help our community identify the most promising automation pathways.