AI use cases for security analysts

AI is transforming the cybersecurity landscape for both threat actors and defenders. Learn how AI is being used on both sides of the battle with practical tips to help your security team up-level its AI use.

Joe DeFever

March 17, 2026

203976_-_Blog_image-_AI_for_security_analysts_WorkingFile.png

Summary

Generic threats increased 15.5% in 2025 as adversaries use LLMs to generate malware loaders and other malicious tools with minimal effort.
AI-powered behavioral analytics, anomaly detection, and automated alert triage help security analysts identify threats in real time and respond faster to novel attacks.
Elastic Security integrates AI capabilities into every SOC workflow to reduce alert noise, accelerate investigations, and keep analysts focused on high-priority threats.
Strategic AI implementation requires auditing existing tools, mapping high-volume tasks for automation, and keeping humans in the loop for critical decisions.

The digital attack surface in the AI era is multiplying rapidly. Launching waves of opportunistic attacks with minimal effort has never been easier. Elastic researchers discovered that generic threats increased by 15.5% in 2025 because of adversaries’ use of large language models (LLMs) to generate low-effort, effective malware loaders and other malicious tools.

To improve their odds against this increased velocity of threats, security analysts must use AI to bolster their defense efforts.

But how exactly should security analysts be using AI to defend against increasingly sophisticated attacks? A strong security posture isn’t just about implementing disconnected AI tools; it comes from crafting a proactive AI strategy to fill in the gaps that your team cannot.

Here we’ll outline common AI use cases for security analysts, exploring how teams can strengthen their defenses against AI-fueled threats and, ultimately, enhance decision-making.

How AI can strengthen cyber defenses

Recognizing, investigating, and countering AI-powered threats depends on frictionless collaboration between technology and human oversight in your SOC.

Key AI-enhanced tools and techniques for security analysts

AI can analyze dynamic patterns in network traffic, user behavior, and system activities in real time. Using AI, your security analysts can identify otherwise undetected anomalies that can indicate a breach.

This technology can help improve your incident response times, ensuring you’re prepared for novel AI threats. Here are some of the top AI-powered security use cases:

Behavioral analytics: AI-powered behavioral analytics establishes normal baselines for users and entities and automatically flags deviations. Using machine learning, these systems can handle massive datasets and provide insights for security analysts in real time.
Anomaly detection: AI enhances anomaly detection by transforming signature-based recognition into a real-time, proactive, and context-aware process of uncovering threats, such as data breaches, compromised accounts, or insider threats.
Automated alert triage and prioritization: AI-driven automated alert triage and prioritization uses machine learning (ML), LLMs, and agents to sort through noisy alerts, surface, and rank active attacks.
Threat intelligence: AI improves threat intelligence by monitoring multiple threat intelligence sources and providing context to help understand threat actors’ latest motives and campaigns.

These AI-enhanced tools shouldn’t just take up space in your SOC — they should earn it by actively boosting your ability to spot AI-generated threats. They help security analysts triage alerts, decipher attack progressions, provide remediation steps, and spot suspicious activity or data patterns before the human eye could.

How AI enhances security analysts’ decision-making

When integrating AI for threat detection and response into your cyber defense toolkit, a human touch remains essential. Skilled security analysts can validate AI insights using their experience, intuition, and deep contextual understanding to make strategic decisions.

However, responding to AI threats means security teams have to make context-rich decisions within minutes. Having AI-driven insights can enhance their ability to make informed decisions efficiently.

How AI supports security analysts

See the full overview of how security analysts use AI in the SOC.
→ Explore now

AI is now a mission-critical part of the cybersecurity stack, helping SOC teams operate with limited resources and perform under constant pressure.

AI supports security analysts by:

Cutting through alert noise
Accelerating investigations
Ingesting and analyzing large amounts of custom data
Automating routine and time-consuming tasks
Navigating SIEM workflows with suggestions
Providing support for junior analysts
Documenting incidents
Suggesting remediation steps
Onboarding data ingest/migration

AI hasn’t changed security analysts’ aptitude at creative threat hunting, interpreting vague signals, anticipating and understanding an attacker’s behavior, and adapting defenses beyond the predefined rules and patterns. Security analysts are still making complex decisions about risk, resource allocation, and specific actions to take in complex threat investigations.

AI emerges to enhance their abilities, but strategic decision-making and human oversight will remain an essential part of the detection and response lifecycle.

Best practices for integrating AI into SOC workflows

Across industries, one of AI’s standout use cases is its ability to streamline workflows. For security analysts, every second counts. AI-enabled SOC workflows keep analysts focused on the most urgent problems: reducing alert fatigue and noise, providing end-to-end visibility, and accelerating threat detection, investigation, and response.

However, it’s not always that simple.

Poor data quality leads to poor AI outputs, and a broken tool ecosystem can’t be remedied by AI tools. Siloed or legacy tools, prohibitive costs, cultural resistance, and the time it may take to identify a trusted and secure LLM can all stall AI integration and success.

To address these challenges, best practices for AI implementation include:

Auditing your existing security tools and evaluating their AI features against desired outcomes
Mapping your security processes to identify high-volume and repetitive tasks that AI could improve or automate
Finding high-impact areas where AI could reduce risk
Evaluating your data’s quality, accessibility, and governance
Finding an InfoSec-compliant LLM and implementing layered security for its usage
Ensuring transparency and governance by creating tight feedback loops for models’ training and defining clear roles for AI agents
Keeping humans in the loop for critical and strategic decisions

By carefully integrating AI into your workflows, your SOC can transition from a reactive model overwhelmed by alerts to an efficient, proactive, and scalable defense system.

AI tools shouldn’t add to your SOC’s workload … they should optimize its workflow. By implementing these best practices for AI in cybersecurity, security analysts can confidently deploy AI tools, investigate and respond to AI-driven threats, and maintain relevant skills.

Elastic Security for AI-driven security analytics

Built on the Elasticsearch Platform, Elastic Security integrates advanced AI capabilities into every SOC workflow.

Elastic Security helps security analysts quiet the noise, focus on what matters, and act fast to defend and secure their organization.

Learn more about AI use for security analysts.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use.

Elastic, Elasticsearch, and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.