Enhancing SOAR Platforms with Machine Learning for Comprehensive Third-Party Risk Management

Introduction

Security Orchestration, Automation, and Response (SOAR) platforms are crucial in managing an organization's cybersecurity landscape by streamlining security operations and reducing response times. Moreover, as organizations grow and increasingly rely on third-party vendors, it is essential to extend the capabilities of these SOAR platforms to address third-party risk management effectively. Integrating a layer of Machine Learning (ML) algorithms into SOAR platforms can boost its ability to manage risks associated with third-party vendors, ultimately strengthening the organization's overall security posture.

Here are specific examples of utilizing ML with SOAR in third-party risk management.

1. Vendor Risk Profiling and Scoring

One of the critical aspects of third-party risk management is the ability to assess and monitor the security posture of vendors. ML algorithms can analyze a massive array of data sources, such as security assessment questionnaires and vendor documentation. Threat intelligence feeds to build comprehensive risk profiles for each vendor by evaluating and calculating different factors, such as the vendor's security controls, data handling practices, and past security incidents, and assigning a risk score to each vendor, enabling organizations to prioritize their efforts in managing third-party risks.

Example: An ML-powered SOAR platform could analyze the security configurations of a cloud service provider and flag potential security gaps, such as weak encryption settings or improperly configured access controls. Updating the vendor's risk score based on these findings and enabling security teams to focus on addressing the most significant risks.

2. Continuous Monitoring and Anomaly Detection

To ensure effective third-party risk management, it is vital to monitor vendor activities continuously. Using ML algorithms, extensive data processes from various sources, such as network traffic, application logs, and API calls, to detect anomalies and potential security incidents in real time. By identifying unusual patterns and behaviors, ML-powered SOAR platforms can alert security teams to potential threats from third-party vendors, allowing for a swift response.

Example: An ML algorithm could analyze API calls between an organization's infrastructure and a third-party payment processing service, identifying a sudden spike in the volume of data being transmitted. This anomaly could indicate a potential data exfiltration attempt, prompting the SOAR platform to alert the security team and initiate an investigation.

3. Incident Response Automation and Collaboration

When dealing with security incidents involving third-party vendors, it is crucial to have efficient and coordinated incident response mechanisms. ML-driven SOAR platforms can automate various aspects of incident response, such as initiating communication with the affected vendor, gathering relevant data, and implementing containment measures. Moreover, ML algorithms can facilitate collaboration between the organization and its vendors by streamlining information-sharing and decision-making processes.

Example: In the event of a data breach involving a third-party vendor, an ML-powered SOAR platform could automatically create a shared incident response workspace. This workspace would enable the organization and the vendor to collaborate on the investigation, communicate evidence, and coordinate containment and remediation efforts.

4. Predictive Analytics for Proactive Risk Management

ML algorithms can analyze historical data to identify trends and patterns that may indicate potential future risks associated with third-party vendors. By leveraging predictive analytics, SOAR platforms can provide organizations with insights into areas that require attention, enabling them to address vulnerabilities and reduce the likelihood of security incidents proactively.

Example: An ML model could identify a pattern of increased security incidents involving vendors with specific attributes, such as those operating in a particular industry or geographic region. This information could help the organization refine its vendor selection criteria, implement targeted security controls, or allocate additional resources to monitor and manage these high-risk vendors.

Conclusion

It is known that machine learning technology is the current revolution; more and more startups are developing advanced technologies to superpower services and solutions. Using ML to superpower third-party risk management can help small and large organizations to identify and protect from the next cyber threat.