Security Automation & Visibility

The automation of workflows in cybersecurity and IT offers a wide range of possible applications that significantly increase the efficiency and effectiveness of organizations. Here are some important use cases:

1. Threat detection and response: Automated workflows can quickly detect and respond to security incidents, reducing response time and minimizing potential damage.
2. Everyday security processes: Routine cybersecurity tasks can be automated to increase efficiency and reduce human error.
3. Security orchestration: Automation can be used as a security orchestration, automation and response (SOAR) solution to coordinate and automate security processes.
4. Compliance management: Automated workflows can help ensure compliance with security and compliance standards by facilitating regular reviews and reporting.
5. Incident response: Predefined workflows enable fast and consistent responses to security incidents.
6. Cross-system processes: Complex, cross-departmental processes can be automated, improving collaboration and efficiency.
7. Self-service automation: User-friendly automation tools enable employees to create and execute their own workflows, reducing the workload on IT.
8. Real-time threat prevention: Automation enables an immediate response to detected threats, improving an organization's security posture.

The use cases show that workflow automation is a versatile tool for improving security and IT processes. It is not important whether the use cases involve thousands of events or are very specific, or whether data from a large number of systems has to be merged or existing information from additional sources has to be enriched in order to be processed further in a meaningful way in a subsequent step.  The solution can be made accessible to both technical and non-technical users.

A cyber asset management system makes it possible to consolidate and manage different types of assets in a central platform. Existing data sources (CMDB, license management, patch solutions, vulnerability scanners, network devices, security solutions, etc.) are used and transferred into a comprehensive data model. These assets include:

1. IT hardware: computers, servers, network devices, mobile devices and IoT devices.
   
2. Software assets: applications, licenses, cloud services and virtual machines.
3. Cloud resources: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS) components.
4. Network assets: switches, routers, firewalls and other network components.
   
5. User accounts and identities: Active Directory records, single sign-on systems, and access permissions.
6. Databases and data stores: Both on-premises and cloud-based solutions.
7. Containers and microservices: Docker containers, Kubernetes clusters, and serverless functions.
8. IoT devices: Sensors, smart devices, and industrial control systems.
   
9. Non-IT assets: physical security systems, building management technologies, and other operational technologies.

Data is typically collected without an additional agent via the respective APIs of the source systems.
By integrating these various asset types into a single platform, organizations can gain a holistic view of their entire technology landscape. This enables improved security, compliance and operational efficiency. In addition, it is easy to trigger automated actions if the data shows an undesirable state (e.g. a missing agent or an unwanted configuration).

Dark web monitoring is the process of systematically searching and continuously monitoring information on the dark web. It is a proactive cybersecurity measure that helps organizations identify and respond to potential threats at an early stage. The dark web is an encrypted portion of the internet that is not indexed by conventional search engines and is only accessible with special software such as the Tor browser. It often serves as a platform for illegal activities, including the trade in stolen data and hacking tools.

Dark Web Monitoring tools continuously scour the Dark Web for specific information such as:

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• Compromised passwords and credentials

• Intellectual property

• Sensitive data

• Personal information of employees or customers

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Advanced search algorithms and automated systems make it possible to monitor millions of websites in real time.

Threat intelligence, also known as cyber threat intelligence (CTI), is the systematic process of collecting, analyzing, and acting on information about cyber threats. It is a proactive approach to cybersecurity that helps organizations identify potential risks early and take appropriate countermeasures. Threat intelligence includes:

1. Identifying threat actors (who?)
2. Analyzing attack methods (how?)
   
3. Understanding motives (why?)
4. Detecting vulnerabilities (which?)

The information collected comes from a variety of sources, including public sources, threat databases and specialized tools. By analyzing this data, organizations can uncover trends, patterns and correlations that provide a comprehensive understanding of actual or potential threats.

A centralized data exchange for risk information enables organizations to manage risk data more efficiently and securely. Such platforms typically collect and provide the following types of data:

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1. Third-party risk assessments: Detailed analyses of potential risks posed by business partners, suppliers or service providers.
2. Compliance information: Data on compliance with industry standards, regulations and legal requirements.
3. Cybersecurity assessments: Assessments of IT security and potential vulnerabilities in digital infrastructure.
   
4. Operational risk factors: Data on operational risks such as production outages or supply chain disruptions.

By providing this data in a centralized location, organizations can:

• Accelerate and standardize risk assessments

• Avoid redundant data collection

• Ensure consistent risk assessment across different business units

• Respond more quickly to changing risk landscapes

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