AI in the SOC: What’s Real vs. Hype in 2025

Posted on 21 April 2025

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Introduction

Artificial Intelligence (AI) has become an inevitable topic in cybersecurity. From vendors promoting AI-driven detection to generative copilots for SOCs, it can appear that a new golden era of security tooling has arrived. However, it is pertinent to pause and ask: What is actually working? What remains theoretical? What risks are we underestimating?

In this post, we explore the present reality of AI adoption in SOCs, highlighting practical implementations, scientific developments, structural barriers, and strategic recommendations.

What’s Real Today: AI Use Cases That Actually Work

Despite the marketing buzz, several AI-driven use cases have demonstrated measurable value in operational environments.

1. Anomaly Detection

Techniques such as clustering, PCA, and autoencoders are widely employed in platforms like Microsoft Sentinel and Vectra AI to identify deviations in user or network behavior.

Reference: A detailed technical overview is presented in AI-Driven Anomaly Detection for Advanced Threat Detection, discussing unsupervised methods for sophisticated anomaly discovery.

2. Alert Scoring and Prioritization

Systems like Chronicle SOAR and QRadar Advisor apply machine learning to assign risk scores by correlating telemetry, user behavior, and threat intelligence, thereby assisting prioritization efforts.

3. Phishing Detection Using Natural Language Processing (NLP)

Solutions such as Abnormal Security leverage NLP models trained on vast corpora to detect linguistic indicators of phishing attempts beyond traditional rule-based detection.

Reference: For an applied study, refer to Phishing Detection Using Natural Language Processing and Machine Learning, which explores how language patterns can distinguish phishing attacks from benign communications.

4. LLM-Powered Investigation Assistants

Tools like Microsoft Security Copilot and Splunk AI Assistant allow analysts to interact with security data using natural language queries, thereby accelerating triage and investigation workflows.

Reference: The concept of an LLM-based security copilot is introduced in Introducing Microsoft Security Copilot.

Reference: A comprehensive literature review on LLMs in cybersecurity is available in Large Language Models for Cyber Security: A Systematic Literature Review.

State of the Art

In parallel to commercial deployments, academic and industrial research is advancing AI capabilities for security operations.

1. Neurosymbolic Reasoning

Neuro-symbolic architectures aim to combine deep learning with logical reasoning for improved contextual decision-making in threat investigations.

Reference: A foundational introduction to this concept is provided in What is Neuro-Symbolic AI?.

Reference: For cybersecurity-specific applications, refer to Neurosymbolic AI in Cybersecurity: Bridging Pattern Recognition and Symbolic Reasoning, which discusses blending symbolic knowledge with ML models to improve detection precision.

2. Few-Shot and Zero-Shot Threat Classification

Few-shot and zero-shot learning techniques allow models to generalize threat detection even when only a limited number of labeled examples are available.

Reference: For an accessible overview, see Exploring Zero-Shot and Few-Shot Learning in Generative AI.

Reference: A domain-specific application in IoT security is demonstrated in Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection.

3. Security-Specific Fine-Tuning of LLMs

Fine-tuning large language models on cybersecurity-specific corpora (e.g., MITRE ATT&CK reports) mitigates hallucination risks and enhances investigative accuracy.

Reference: Lessons on principled fine-tuning for security tasks are presented in Can Safety Fine-Tuning Be More Principled? Lessons Learned from Cybersecurity.

Reference: For benchmark evaluation on LLM security defenses, refer to Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents.

4. Graph Neural Networks (GNNs) for Threat Correlation

GNNs allow modeling of entities, relationships, and multi-domain telemetry for automated correlation of attack sequences.

Reference: An intuitive introduction is available in A Gentle Introduction to Graph Neural Networks.

Reference: For practical threat hunting applications, see DeepHunter: A Graph Neural Network Based Approach for Robust Cyber Threat Hunting.

5. Human-in-the-Loop (HITL) AI Systems

Human-in-the-Loop (HITL) systems integrate human analyst validation and feedback to dynamically refine AI decision boundaries.

Reference: A conceptual overview is available at What is Human-in-the-Loop (HITL) in AI & ML?.

Reference: A systematic survey on HITL practices in machine learning is presented in A Survey of Human-in-the-Loop for Machine Learning.

Why Most SOCs Aren’t Ready for AI

Despite promising advances, most Security Operations Centers are not fully prepared to operationalize AI effectively. Common barriers include:

1. Telemetry Is Inconsistent, Incomplete, or Noisy

Logs from different sources often lack consistency in format, richness, and contextual fields, complicating downstream model training.

Reference: For an in-depth overview of challenges associated with cybersecurity big data, refer to Big Data and Cybersecurity: A Review of Key Privacy and Security Challenges.

Reference: A practical exploration of telemetry improvements for SOCs is provided in The Case for Data Thinking in the SOC.

2. Lack of Quality Labels

Without accurate and consistent labeling (e.g., true positive, false positive, false negative), supervised machine learning becomes impractical, leading to unreliable model outcomes.

Reference: A discussion on labeling methodologies in cybersecurity is available in Understanding the Process of Data Labeling in Cybersecurity.

3. Siloed Data Across Tools

Data fragmentation across SIEMs, SOAR platforms, CTI feeds, and EDRs prevents unified threat modeling and reduces AI efficacy.

Reference: For an overview of this challenge, see What is Data Fragmentation? 8 Strategies to Solve & Combat.

4. No Analyst Feedback Loops

Without formal feedback mechanisms, AI systems cannot adapt to new threat behaviors, leading to model drift and decreasing detection performance over time.

5. Organizational Resistance: Automation Anxiety

Cultural factors such as mistrust of automated decisions, fear of obsolescence, or resistance to process change hinder the successful adoption of AI in security operations.

6. Lack of Explainability (XAI)

SOC analysts require transparency into why AI models classify alerts in certain ways in order to maintain operational trust and validate recommendations.

Reference: Foundational concepts of explainable AI are discussed in What is Explainable AI?.

7. Absence of MLOps Practices

Operational management of AI — including retraining cycles, performance monitoring, model rollback capabilities, and concept drift detection — remains underdeveloped in most security environments.

My Recommendations for 2025

To facilitate successful and responsible adoption of AI in security operations, a phased approach is advised.

Immediate Wins (0–6 Months)

• Implement lightweight analyst feedback capture mechanisms (e.g., “Good/Bad Alert” classifications during triage).
• Standardize and normalize telemetry fields across diverse data sources.
• Develop baseline SOC operational dashboards measuring critical metrics such as offense volumes, Mean Time to Detect (MTTD), and Mean Time to Respond (MTTR).

Medium-Term Goals (6–12 Months)

• Establish a centralized security data warehouse or lake capable of aggregating multi-domain telemetry (endpoint, network, identity, cloud).
• Pilot machine learning applications in narrow, controlled domains such as phishing detection, false positive reduction, or log anomaly clustering.
• Prioritize the evaluation and adoption of AI tools offering explainable outputs that analysts can validate.

Long-Term Vision (12–24 Months)

• Design and operationalize Human-in-the-Loop feedback loops at critical decision points in detection and incident response workflows.
• Fine-tune internal large language models (LLMs) on organization-specific security telemetry, incident reports, and standard operating procedures (SOPs).
• Formalize MLOps practices within the SOC environment, including retraining intervals, drift detection monitoring, rollback testing, and automated health monitoring of AI systems.

“Succeeding with AI is not a matter of technology adoption alone — it is a matter of cultural readiness, data governance, operational discipline, and human-machine collaboration.”

Key Concepts Explained

Anomaly Detection

The identification of unusual patterns in telemetry that significantly deviate from established behavioral baselines. It is a critical technique in systems such as Network Detection and Response (NDR), Endpoint Detection and Response (EDR), and User Behavior Analytics (UBA).

Neurosymbolic Reasoning

An AI methodology that combines the pattern recognition capabilities of neural networks with the logical inference strengths of symbolic systems. Neurosymbolic AI enables reasoning over structured cybersecurity knowledge graphs, improving detection contextualization.

Reference: For a foundational overview of this emerging field, refer to What is Neuro-Symbolic AI?.

Few-Shot and Zero-Shot Learning

Machine learning paradigms where models generalize from few or no labeled examples. These approaches are especially critical in security operations where novel threat types emerge more rapidly than labeled datasets can be created.

Reference: For applied methodologies, see Exploring Zero-Shot and Few-Shot Learning in Generative AI.

Graph Neural Networks (GNNs)

A class of neural networks designed to operate on graph-structured data. GNNs are particularly well-suited for modeling interconnected security entities such as assets, users, credentials, and threat indicators.

Reference: A conceptual introduction can be found at A Gentle Introduction to Graph Neural Networks.

Human-in-the-Loop (HITL)

An AI design pattern that actively incorporates human analyst validation, correction, and feedback into machine learning workflows, ensuring models adapt to evolving threats and human-driven operational contexts.

Reference: An academic survey of HITL methodologies is available in A Survey of Human-in-the-Loop for Machine Learning.

Large Language Models (LLMs)

Deep learning architectures trained on massive text corpora capable of performing diverse natural language processing tasks, including summarization, classification, reasoning, and translation. Fine-tuned LLMs are now being explored for cybersecurity-specific use cases.

Reference: Foundational principles are described in Language Models Are Few-Shot Learners.

Explainable AI (XAI)

Research efforts aimed at making AI model decisions transparent and interpretable to human operators. Explainability is especially important in high-stakes environments such as security operations where trust and validation are critical.

Reference: Practical implementations of XAI concepts in security are discussed in What is Explainable AI?.

Stochastic Parrots Problem

A phenomenon where large language models generate syntactically correct but semantically inaccurate outputs due to their reliance on statistical pattern matching rather than true understanding.

Reference: The challenges posed by this issue are discussed in On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?.