How machine learning and generative AI transform threat detection and SOC operations

The rapid evolution of cyber threats has necessitated the adoption of advanced technologies to enhance threat detection and response capabilities. Supervised and unsupervised machine learning, and generative AI have emerged as transformative tools in cybersecurity, significantly altering how Security Operations Centers (SOCs) operate.

These technologies enable faster, more accurate threat detection and response, while reducing the workload on human analysts.

Supervised machine learning in threat detection

Supervised machine learning (ML) relies on labeled datasets to train models that can classify data or predict outcomes. In cybersecurity, this approach is particularly effective for identifying known threats based on historical attack patterns.

Applications in threat detection

1. Malware classification:
   • Supervised ML models are trained on datasets of known malware signatures and behaviors. This enables them to classify incoming files or activities as malicious or benign with high accuracy.
   • For example, these models can detect phishing emails by analyzing features like sender reputation, email content, and attachment types.
2. Intrusion detection systems (IDS):
   • Supervised ML enhances IDS by identifying deviations from normal traffic patterns that match known attack signatures.
   • This allows for real-time alerts when specific attack vectors, such as SQL injections or Distributed Denial of Service (DDoS) attacks, are detected.
3. Real-time anomaly detection:
   • Pre-trained machine learning models establish complex baselines of normal activity across multiple dimensions (e.g., network traffic, user behavior). They can identify subtle deviations that indicate sophisticated attacks such as zero-day exploits.

Impact on SOCs

• Increased Efficiency: By automating the detection of known threats, supervised ML reduces the time SOC analysts spend on repetitive tasks like malware classification.
• Limitations: The reliance on labeled data means supervised ML struggles with identifying novel or zero-day threats, requiring complementary approaches like unsupervised learning.

Unsupervised machine learning in threat detection

Unsupervised machine learning does not rely on labeled data but instead identifies patterns and anomalies within datasets. This makes it particularly useful for detecting previously unknown threats.

Applications in threat detection

1. Anomaly detection:
   • Unsupervised ML models establish baselines of “normal” behavior within network traffic or user activity. Deviations from these baselines are flagged as potential threats.
   • For example, unusual login times or access to sensitive files from unexpected locations can trigger alerts.
2. Behavioral analytics:
   • These models analyze user behavior to detect insider threats or compromised accounts by identifying unusual actions that do not fit typical usage patterns.
3. Entity resolution:
   • Identify and merge records that refer to the same entity across different datasets—by leveraging clustering and similarity-based techniques without requiring labeled training data.

Impact on SOCs

• Proactive defense: Unsupervised ML enables SOCs to detect emerging threats that lack historical data or predefined signatures.
• Reduced false positives: By refining anomaly detection over time with dynamic baselines adjusted continuously so unsupervised machine learning models adapt to changing circumstance and the resulting new norms, automatically, resulting in reduction inthe number of false alarms that analysts must investigate.
• Challenges: The lack of labeled data can lead to difficulty in contextualizing anomalies, requiring human intervention to validate alerts. Recent development in leveraging correlation techniques to generate Behavioral Threat Indicators and generative AI are starting to alleviate this burden.

Generative AI in threat detection

Generative AI represents a significant leap forward by leveraging deep learning techniques to create predictive models and simulate scenarios. Its ability to analyze vast datasets and generate synthetic data makes it a powerful tool for threat detection.

Applications in threat detection

1. Virtual assistant:
   • Analyzes vast amounts of security data from various sources, using natural language processing to generate insights, summaries, and actionable recommendations to assist security analysts in tasks like threat hunting, incident response, and posture management.
   • Helps SOCs identify and address potential security issues faster and more efficiently; this includes summarizing complex incidents, providing remediation steps, and highlighting critical details from large data sets, all through a user-friendly natural language interface.
2. Threat contextualization:
   • Generative AI enhances situational awareness by correlating data from various sources to provide detailed insights into a threat’s origin, target, and potential impact.
   • For example, when a new type of malware is detected, generative AI can predict its behavior based on similarities with known malware families.
3. Synthetic data generation:
   • Generative AI creates synthetic datasets to simulate attack scenarios, enabling organizations to test their defenses against emerging threats without exposing real systems to risk.

Impact on SOCs

• Enhanced decision-making: By providing contextualized insights into threats, generative AI enables SOC teams to make faster and more informed decisions.
• Automation of low-level tasks: Generative AI automates repetitive tasks like IP analysis and risk assessment, freeing analysts to focus on strategic initiatives.
• Proactive defense: Its predictive capabilities transform cybersecurity from a reactive measure to a proactive system that anticipates attacks before they occur.
• Dual-Use risks: Generative AI’s capabilities can also be exploited by threat actors to create sophisticated attacks like deepfakes or automated phishing campaigns.

Comparative impact on SOC operations

The integration of these technologies has redefined how SOCs operate by improving efficiency, accuracy, and scalability.

Feature	Supervised ML	Unsupervised ML	Generative AI
Threat Identification	Known threats based on historical data	Unknown threats via anomaly detection	Predictive identification of novel threats
False Positive Reduction	Moderate	High	Very High
Automation Level	Moderate	High	Very High
Proactive Capabilities	Limited	Moderate	Extensive
SOC Analyst Workload	Reduced for repetitive tasks	Reduced for anomaly investigations	Significantly reduced through automation
Compute Workload	Medium – Ideal for detection of known threats such as malware	High – Ideal for unknown threat detection.	Very High – Ideal for context enrichment and decision assistance.
Challenges and Mitigations	Requires labeled data – Unsupervised Machine Learning provides the essential complementary capability.	Difficult anomaly contextualization – BTI (Behavioral Threat Indicator) and generative AI tackle this issue.	Dual-use risks and computational costs – Rapidly evolving detection LLMs and increasingly efficient techniques address these concerns.

How OpenText can help your SOC team

Learn more about how OpenText is using machine learning and generative AI alongside behavioral analytics to help companies build stronger, more efficient, accurate, and scalable SOC teams.