Cybersecurity threats have rapidly grown in sophistication, with rogue nation-states and well-financed organized crime groups launching advanced persistent threats. Therefore, maintaining robust cybersecurity is a challenge, even for well-resourced enterprise organizations.
Automated threat hunting helps organizations level the field using advanced technologies and algorithms. These automated tools proactively gather threat intelligence by continuously monitoring network activity to nix anomalous patterns and suspicious behavior before they spread.
What Is Automated Threat Hunting, and How Does It Differ from Traditional Threat Hunting?
Automated threat hunting refers to the use of artificial intelligence (AI), machine learning (ML), and tools that proactively identify potential cybersecurity threats. It leverages automation to analyze high volumes of data, detect anomalies, and initiate responses at a speed and scale that is impossible with manual efforts alone.
On the other hand, traditional threat hunting is a manual process carried out by human analysts who proactively search through networks, databases, and systems to identify signs of compromise or vulnerabilities that automated systems might miss.
While traditional threat hunting involves extensive knowledge and human intuition to identify subtle signs of threats, automated threat hunting utilizes advanced algorithms and patterns to uncover potential threats.
Both methods are critical in a comprehensive cybersecurity strategy, as humans provide the intuition and critical thinking necessary to find sophisticated, targeted campaigns. In contrast, automation offers the scalability to analyze vast quantities of data for known threat patterns.
The Key Benefits of Implementing Automated Threat-Hunting Solutions
Even on their own, automated threat-hunting processes bring significant benefits to the table that, plainly, human threat hunters can't necessarily provide on their own. Some of these benefits include:
Streamlined Detection: Automated threat hunting can detect threats more efficiently and effectively using machine learning and AI algorithms, which can analyze huge volumes of data faster than human beings.
Reduced Manual Effort: Automating repetitive tasks such as data collection and analysis saves cybersecurity experts time, allowing them to focus more on complex threat analysis and resolution.
Faster Response: Automated threat-hunting solutions can identify and respond to potential security incidents much faster, reducing the dwell time and potential damage of threats.
Continuous Monitoring: These solutions provide 24/7 monitoring of systems and networks, ensuring immediate detection of any potential threats.
Cost-Effective: By automating tasks and increasing the speed of threat detection and response, businesses can save on the costs associated with data breaches and downtime.
Consistency: Unlike manual processes, automated threat hunting provides consistency in detecting and handling threats, reducing the likelihood of human error.
Enhanced Reporting and Analytics: With automated tracking and reporting, businesses can gain a better understanding of their threat landscape and cybersecurity performance.
Reducing the Skills Gap: Automation can help mitigate the effect of a cybersecurity skills shortage by handling routine tasks, allowing the existing staff to focus on more strategic, high-level tasks.
Streamlined Compliance: Automated solutions can help enterprises meet regulatory compliance mandates by providing consistent, automated responses and reporting for security incidents.
Why Automation Is Important In the Threat-Hunting Process
With the above benefits in mind, however, automation should complement—not replace—human participation in threat hunting. Human analysts provide the critical thinking and intuition necessary for effective threat hunting. But even so, automation can directly assist organizations and their human threat hunters in their independent processes. Some of these benefits include:
Efficiency
Automation allows for rapidly analyzing vast amounts of data significantly faster than any human analyst could manage. This speed is crucial in threat hunting, where time is often of the essence.
Accuracy
Automated systems can help reduce human error. They can consistently perform checks and analyses without the mistakes of manual processes.
Scalability
As networks expand and become more complex, automation allows organizations to keep up with data's increased volume and complexity. Automated processes can quickly scale and be adjusted as needed.
Resource Allocation
By automating routine tasks, organizations free up their cybersecurity teams to focus on more strategic tasks. This could be learning about new potential threats, improving current processes, or tackling detected threats.
Reducing Dwell Time
Automation tools can help detect indicators of compromise or malicious activity faster and shorten the dwell time, which is the time between an attacker gaining access to a network and their detection.
Proactiveness
Automation helps shift the security approach from reactive to proactive, enabling identifying and resolving threats before they become incidents.
Continuity
Automated systems can work 24/7, providing constant monitoring and alert systems without the need for human intervention.
What Types of Threats Can Automated Threat Hunting Detect?
Just as human threat hunters are trained to combat a wide variety of cyber threats, automation tools can do the same. While the effectiveness of detecting these threats is highly dependent on the quality of the algorithms used in the automation and the data set provided for analysis, such tools are often built to detect many or all of the following threats:
- Advanced Persistent Threats (APTs): These are stealthy, continuous hacking efforts, typically carried out by attackers targeting a specific entity.
- Malware: It includes viruses, worms, trojans, ransomware, spyware, adware, and botnets.
- Insider Threats: These are threats originating from within the organization, such as employees or associates with malicious intent.
- Zero-Day Exploits: These attacks occur the same day a weakness is discovered in software.
- Anomalous Behavior: Automated threat hunting can detect unusual behavior within a network that may indicate a cyber attack.
- Data Exfiltration: This detects attempts to export data to an external location.
- Credential Theft: Identifying unauthorized attempts to access and misuse user credentials.
- Phishing Attacks: Automated systems can detect suspicious email activity and URLs associated with phishing attacks.
How Can Organizations Integrate Automated Threat Hunting Into Their Cybersecurity Strategy?
Every organization is starting from a different place in terms of their threat-hunting capabilities: some may already have existing tools in place while others have only relied on human threat hunters up to this point. It's important for any given organization to first assess its threat-hunting capabilities, along with what is and isn't automated in its current processes, and then implement the following approaches as they see fit:
Enhance Existing Security Tools
Integration starts with enhancing existing security tools with automated threat-hunting technology. This involves using AI and machine learning to enrich the functionality of existing secure collaboration tools, such as Security Information and Event Management (SIEM), Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) solutions.
Leverage Automation and AI
AI, machine learning, and even deep learning technologies can quickly sort through enormous datasets to identify patterns and anomalies that may signify a threat, allowing organizations to respond quicker than manual methods.
Integrate Threat Intelligence
Threat intelligence feeds can be combined with automated threat-hunting tools to gather information about new and emerging threats. This proactive approach helps identify threats before they can affect the organization.
Scale Up Efforts
Use automation to scale threat-hunting efforts across the entire IT infrastructure. Automated threat-hunting tools can monitor all endpoints and network traffic round-the-clock, ensuring no potential threat is missed.
Regularly Update Threat Hunting Tools
Cyber threats evolve, and threat-hunting tools should, too. Regular updates ensure that the AI algorithms can detect the latest threats.
Opt for a Hybrid Approach
Combine automated threat hunting with manual techniques for a more balanced approach. While automation speeds up the process and reduces the workload, human expertise is still necessary for strategic decision-making and understanding the broader context of threats.
Staff Training
Ensure that security team members are trained in using automated threat-hunting tools, understand the output, and know how to act on the results produced by these tools.
Define Clear Workflow Processes
Have clear processes for automation, analysis, setting rules/alerts, incident response, and remediation to ensure seamless integration.
Continuous Monitoring
Automated tools are used to monitor network activities and behaviors continuously to identify deviations from the norm that could indicate a security risk.
Measure and Review
Regularly measure the effectiveness of the automated threat-hunting integration and make any necessary adjustments to improve outcomes.
How Machine Learning and AI Contribute to Automated Threat Hunting
As ML and AI continue to rapidly develop, they have become integral components of automated threat-hunting strategies, including in the following ways:
- Automated Detection: ML and AI algorithms can learn from historical data to identify potentially harmful patterns and anomalies. They can flag unusual network behavior or file activities, enabling quicker detection of potential threats.
- Real-Time Analysis: AI can process enormous quantities of data in real time, a process that manual threat hunting would struggle to keep up with. This allows for faster response times when a threat is detected.
- Predictive Analysis: With their ability to learn from previous data and incidents, AI and ML can predict possible attack vectors and identify vulnerabilities even before they are exploited.
- Reducing False Positives: AI and ML can learn to distinguish between normal network behavior and potential threats with increasing accuracy over time, reducing the number of false positives and allowing cybersecurity teams to focus on real threats.
- Enhanced Threat Intelligence: AI can enhance threat intelligence capabilities by automatically correlating threat information from various sources and providing a more comprehensive view of the threat landscape.
- Incident Response: AI-powered systems can also assist in incident response by suggesting or even automating appropriate steps to mitigate the detected threats.
The Challenges of Deploying Automated Threat-Hunting Tools
While automation can significantly benefit organizations' overarching threat-hunting efforts once tools and strategies are in place, that doesn't mean their implementation is free of challenges. IT teams can encounter a myriad of road bumps, including:
- Complexity: Data's sheer volume and complexity can be hard to manage. The tools must be configured properly to collect, analyze, and correlate data effectively.
- Skill Gap: Automated threat hunting requires specialized skills. Many organizations lack professionals with the knowledge to operate and interpret the results from these advanced tools.
- Integration Issues: Automated threat-hunting tools must integrate seamlessly with other security systems. Integration issues can limit the tool's effectiveness.
- Cost: The tools typically require significant investment in terms of purchase, deployment, and maintenance.
- High False Positives: Automated systems can result in a high number of false positives, putting an unnecessary burden on the already strained security staff.
- Evolving Threat Landscape: Cyber threats evolve constantly. Thus, the automated threat-hunting tools need to be updated regularly to cope with new threats.
- Data Privacy: Using automated tools may also raise data privacy concerns, as confidential company information often needs to be shared with the tool's vendor.
- Over-Reliance on Automation: Relying heavily on automated threat-hunting systems can lead teams to neglect basic, but equally important, security practices.
- Customization and Adaptability: Every organization is unique, having its own set of infrastructures, applications, and potential vulnerabilities. Thus, automated threat-hunting tools may need customization to be effective in differing environments.
Learn How Digital Guardian DLP Automates Threat Hunting
Automated threat hunting can significantly alleviate the burden of an organization’s cybersecurity staff. However, with automation handling routine and repetitive tasks, they can focus more on complex threat analysis and enhance the overall security posture.
Enter Fortra's Digital Guardian, who not only brings decades of data protection experience to the table, but whose DLP platform utilizes machine learning, contextual intelligence, and threat intelligence insights from the Fortra Threat Brain for integrated threat detection and response.
Schedule a demo with us today to learn more.
