Addressing Threat Hunter Burnout: The Limitations of Existing SIEM and SOAR Products
Threat hunting is an essential activity for organizations to detect and respond to security incidents effectively. However, the reliance on existing Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) products often leads to burnout among threat hunters. Despite the introduction of security automation tools like Tines and Torq, the challenges persist, as they require high maintenance of rules and may overlook critical security incidents. In this blog post, we will explore the problem of threat hunter burnout and the limitations of existing automation products.
The Problem of Burnout
Threat hunting is an intricate task that demands continuous monitoring, analysis, and response to potential security threats. The high volume of alerts generated by all of the various security products overwhelms threat hunters, resulting in alert fatigue and increased stress levels. The manual effort required to investigate each alert, validate its legitimacy, and respond accordingly can be exhausting and time-consuming. We have observed this phenomenon across the board when we talk to SOC teams at all sizes of companies.
The Limitations of SIEM and SOAR Products
SIEM and SOAR products have been instrumental in centralizing security event data and automating certain response actions. However, they still fall short in alleviating the burden on threat hunters due to several limitations:
- Strict Rule-based Approach:
Many existing automation products rely heavily on strict, predefined rules to triage alerts and trigger actions. This approach necessitates constant maintenance and updates to ensure accuracy and relevance. Threat hunters often find themselves spending significant time and effort fine-tuning rules, which can contribute to burnout and hinder their ability to focus on critical threats.
- Missed Security Incidents:
Despite the use of automation, there is always a risk of missing security incidents. The complexity and evolving nature of threats makes it challenging to define rules that cover all possible scenarios. Threat actors continually adapt their tactics, techniques, and procedures (TTPs) to evade detection, making it crucial to have a proactive and adaptable approach that goes beyond rule-based automation.
- Insufficient Incident Response:
While automation tools can assist in streamlining certain response actions, they often lack the context and decision-making capabilities that human threat hunters possess. Complex security incidents require nuanced analysis, decision-making, and creative problem-solving, which are difficult to replicate through rigid automation. Consequently, the response to incidents may be inadequate, leading to potential breaches or extended incident response times.
A Holistic Approach to Address Burnout
To address threat hunter burnout and maximize the efficiency of security operations, organizations need to adopt a holistic approach that combines automation with human expertise:
- Intelligent Automation:
Embrace advanced automation technologies that leverage machine learning and artificial intelligence. These technologies can augment threat hunting by autonomously analyzing large volumes of security data, identifying patterns, and prioritizing alerts for investigation. By reducing false positives and providing meaningful insights, intelligent automation empowers threat hunters to focus on high-value activities. This is where Cybermonic’s Knowledge Graph is very helpful – it uses Artificial Intelligence along with Graph theory to visualize the relevant data, make it easier to explore and investigate the threats, as well as to apply comprehensive and holistic responses.
- Adaptive Threat Detection:
Implement adaptive threat detection techniques that go beyond rule-based approaches. Behavioral analytics, anomaly detection, and machine learning models can detect and flag suspicious activities based on deviations from normal behavior patterns, enabling threat hunters to proactively investigate potential threats. Cybermonic’s automated triaging is based on machine learning models that help security analysts identify anomalies in a visual way that surfaces high-priority incidents.
- Continuous Training and Skill Development:
Invest in ongoing training and skill development programs for threat hunters. This includes staying updated on the latest threat landscape, emerging attack techniques, and advancements in security technologies. Equipping threat hunters with the right knowledge and tools enhances their ability to detect and respond to sophisticated threats effectively. Compared to today analysts have to do most of the triage work in a manual fashion, using Cybermonic empowers less experienced analysts to see incidents and act on them in a timely way with less support required from senior personnel. It also helps reduce the burnout of senior analysts who are tired of sifting through massive amounts of data across many products.
- Collaboration and Knowledge Sharing:
Encourage collaboration among threat hunters, both within the organization and through participation in external communities and forums. Sharing experiences, best practices, and lessons learned can help alleviate the isolation and stress often associated with threat hunting. Collaboration also facilitates collective intelligence and fosters a culture of continuous improvement.
Summary
Threat hunting is a complex and critical activity in cybersecurity, requiring advanced tools and techniques to stay one step ahead of adversaries. The Cybermonic Knowledge Graph offers a robust solution for enhancing threat-hunting capabilities. By leveraging its contextual insights, cross-domain analysis, automated recommendations, and continuous learning, security teams can proactively identify and mitigate threats. Integrating the power of the Cybermonic Knowledge Graph into threat-hunting workflows enables organizations to bolster their cybersecurity posture and protect their digital assets in an increasingly challenging threat landscape.
About Cybermonic
Cybermonic was founded by researchers with PhDs in graph systems, graph analytics, graph AI/ML, and a track record of DARPA funded research on cybersecurity challenge problems. They have perfected their graph systems and graph algorithms in order to supercharge cybersecurity analysts.