See for yourself how Prophet AI can supercharge your security operations, accelerating alert investigation and response
Key benefits:
Lowers MTTR with AI-driven automated alert triage & investigation
Lowers risk by prioritizing critical alerts for analyst review
Eliminates manual effort, freeing analysts to focus on high-impact security tasks
-min.png)
-min.png)
-min.webp)
In an era where cyber threats are growing both in volume and sophistication, the role of Security Operations Center (SOC) Analyst has never been more critical. This role – sometimes referred to as Security Analyst, Security Engineer, or Security Operations Engineer – is critical to detecting and responding to threats quickly.
As organizations grapple with an ever-expanding threat landscape, AI SOC Analysts, or Cybersecurity AI Assistants as Gartner refers to them, are emerging as valuable assets to enhance security operations. This guide aims to help you understand and evaluate AI SOC Analysts, shedding light on how they can bolster your cybersecurity posture.
SOC analysts are the first responders in the battle against cyber threats. They are responsible for triaging and analyzing security alerts, identifying potential threats, and prioritizing them based on severity. Their vigilance safeguards sensitive data and ensures compliance with regulatory standards, acting as the cornerstone of an organization's cybersecurity program.
By swiftly addressing potential security issues, SOC analysts prevent disruptions that could cripple business operations.
One of the most significant challenges SOC analysts face is alert overload. The high volume of security alerts can lead to fatigue, increasing the risk of missed threats. This overwhelming workload makes it difficult to distinguish between genuine threats and false positives.
SOC analysts often engage in time-consuming, repetitive tasks such as log analysis and routine monitoring. These manual processes reduce efficiency and can divert attention from more strategic initiatives.
The demanding nature of the job leads to high stress levels and burnout, contributing to high turnover rates. Resource constraints further exacerbate these issues, making it challenging to maintain a skilled and experienced team.
For SOC analysts, the pain isn't just the flood of alerts—it's the lack of effective tools to handle them. Solutions like SOARs promise automation but require a large and ongoing investment to build and maintain playbooks and integrations. SIEMs centralize security data management but can often contribute to alert overload.
This leaves SOC analysts buried in a sea of notifications, chasing false positives and piecing together fragmented data. Instead of having a tool that aggregates and prioritizes alerts intelligently, analysts are left stitching together data manually, leading to inefficiencies and missed opportunities to catch real threats faster.
Operational silos slow everything down. SOC analysts can't adjust detections on the fly—everything goes through a separate detection engineering team. In parallel, without direct insight into the triage and investigation process, engineers often don’t know how their detections play out in real-time. They lack the visibility into what analysts face daily, which leads to imperfect tuning and misaligned priorities.
The threat landscape is constantly changing and organizations want to add new detections, particularly around Cloud and Identity. However, they are unable to do so as they are constrained by their team’s capacity to triage and investigate more alerts. As a result, they don’t add these detections which ultimately adds risk for the organization.
Delays or oversights resulting from the aforementioned challenges can lead to security breaches. The inability to promptly detect and respond to threats increases the organization's vulnerability.
Inefficiencies in the SOC lead to increased expenses and resource allocation issues. The cost of managing a high volume of alerts and turnover among analysts can strain budgets.
These challenges highlight the necessity for tools that augment analyst capabilities, making their work more efficient and less prone to error.
The introduction of AI SOC Analysts marks a transformative moment in cybersecurity. Rather than replacing human analysts, AI serves to augment their capabilities. AI and LLMs in particular excel at automating manual tasks and analyzing vast amounts of data quickly. Conversely, humans bring intuition, experience, and ethical judgment to the table. This synergy creates a more robust defense mechanism against cyber threats.
An AI SOC Analyst is an advanced system that leverages artificial intelligence technologies—including Machine Learning (ML), Large Language Models (LLMs), and agentic architectures—to automate manual, repetitive tasks traditionally handled by human analysts.
These tasks encompass threat detection, alert triage and investigation, recommending tasks for incident response recommendations, and even automating remediation in certain scenarios.
Unlike chatbots or AI copilots that require human input for every action, AI SOC Analysts can function proactively—planning, reasoning, and making decisions independently, much like specialized digital assistants in the cybersecurity realm.
Traditional security tools such as SOARs often operate based on predefined rules and require constant human oversight. Similarly, AI copilots are reactive tools designed to augment human decision-making in real-time but are limited by the need for human input. In contrast, AI SOC Analysts embody the characteristics of AI Agents:
Autonomy: They are proactive systems capable of planning, reasoning, and making decisions independently, functioning like proactive digital assistants rather than just reactive tools.
Complexity: AI SOC Analysts have more complex architectures that orchestrate multiple specialized tasks, requiring advanced AI expertise and deep domain knowledge in cybersecurity.
Capabilities: They can handle entire tasks independently, including planning, learning, and reasoning, effectively acting as specialized assistants within the SOC.
Scalability: Operating 24/7, AI SOC Analysts can manage multiple tasks simultaneously without the limitations imposed by human availability.
Integration: They can function as standalone systems or seamlessly integrate across various platforms and existing security tools within the organization.
Decision-making: AI SOC Analysts make complex decisions based on multiple data points, feedback, and learned patterns, going beyond offering suggestions to taking informed actions.
Business impact: By enabling round-the-clock productivity and allowing human analysts to focus on strategic tasks, AI SOC Analysts have the potential to revolutionize security operations, rather than merely providing an incremental productivity boost.
By incorporating these advanced capabilities, AI SOC Analysts redefine the role of AI in cybersecurity, moving from supportive tools to proactive agents that can significantly enhance the effectiveness and efficiency of security operations.
The efficacy of AI SOC Analysts is underpinned by several advanced technologies:
Agentic architecture: This refers to AI systems designed with autonomy in mind, allowing them to perform tasks without continuous human guidance. Agentic architectures enable AI SOC Analysts to plan, execute, and adapt their actions based on the evolving threat landscape.
Large Language Models (LLMs): LLMs process and generate human-like text, enabling the AI to interpret unstructured data, understand context, and communicate findings effectively. This is crucial for analyzing alerts, threat intelligence feeds, and other contextual data.
Machine Learning (ML): ML algorithms learn from historical data to identify patterns, enhancing threat detection capabilities. They enable the AI SOC Analyst to adapt to new threats by learning from previous incidents.
Integration with security tools and workflows: AI SOC Analysts are designed to seamlessly integrate with existing security infrastructure and data sources, including security information and event management (SIEM) systems, extended detection and response (XDR) tools, endpoint detection and response (EDR) tools, security data lakes, identity providers (IDP), cloud platform providers, cloud security tools, and collaboration tools. This integration ensures that they can access the necessary data and execute responses effectively.
AI SOC Analysts essentially eliminate the manual, tedious, and repetitive tasks associated with triaging and investigating alerts, helping SOC teams focus their limited resources on most critical security issues.
With AI performing initial alert investigation, mean time to investigate and mean time to respond are shortened, enabling quicker mitigation of potential breaches.
Instantaneous triage and investigation also means you can investigate all the low / medium severity alerts that otherwise would be ignored, and uncover hidden threats among the noise.
AI operates 24/7 without fatigue, ensuring continuous monitoring. This constant vigilance enhances the organization's ability to detect and respond to threats at any time.
Automation of routine tasks leads to lower operational expenses. Organizations can reallocate resources to strategic areas, optimizing budget utilization.
The capacity advantage of an AI SOC Analyst means teams can add additional detections that were out of reach due to resource constraints. This maximizes the effectiveness of security tools and the return on investment. The enhanced capabilities lead to better protection without proportionally higher costs. Additionally, AI frees analysts from mundane tasks, allowing them to focus on strategic initiatives like threat hunting and security architecture improvements.
Not all AI SOC Analysts are created equal, and their effectiveness depends on several factors.
We recommend the following in order to cut through vendor marketing hype and inflated expectations:
It’s crucial to understand the security operations use cases for AI and set your objectives accordingly. An AI SOC Analyst should have a measurable impact on key SOC metrics, so identifying those metrics upfront, and aligning them with the evaluation process will increase chances of success.
One of the first questions to consider when evaluating an AI SOC Analyst is whether its decisions are understandable. AI systems should provide clear explanations for their recommendations to build trust and provide all the underlying evidence. Transparency is essential for analysts to understand and validate AI-generated insights.
An effective AI SOC Analyst should accurately identify true positives and false positives. The depth, breadth, and relevance of the investigative questions that an AI SOC Analyst autonomously answers will determine its accuracy and reliability.
Since the analyst’s ability to detect genuine threats is paramount, you should put a premium on the depth and accuracy of its investigations. This is also where red teaming exercises can be helpful during a proof-of-concept (POC).
AI systems must be capable of learning from new data, evolving threats and analyst feedback. Continuous learning ensures that the AI remains effective and adapts to your organization over time.
Data leakage remains a key security concern with LLMs. Organizations must ensure that an organization’s sensitive data is not used to train or fine tune overall AI models, eliminating data leakage risks. A single tenant architecture with the option to deploy the data plane in your own VPC or cloud provides additional data security and control.
Seamless integration with current SOC tools is essential for accurate and effective investigations. Check to make sure that integrations are available or can be added quickly. The ability to integrate the results with your existing workflows is essential for adoption. AI SOC Analysts should enhance, not disrupt, existing workflows.
The introduction of AI SOC Analysts into security operations represents a significant advancement in cybersecurity. By addressing the challenges faced by traditional SOCs, AI offers enhanced efficiency, accuracy, and a stronger security posture. However, successful implementation requires careful evaluation, planning, and a balanced approach that combines the strengths of both AI and human analysts.
At Prophet Security, we offer an AI SOC Analyst that applies human-level reasoning and analysis to proactively triage and investigate every alert. Request a demo today to see how Prophet AI can help your security operations team.
.png)
Ajmal Kohgadai
.svg)
As the Director of Product Marketing at Prophet Security, Ajmal drives marketing and growth strategies and helps security professionals see how AI is transforming security operations.
.svg)