Trusting AI SOC Agents with Mission-Critical SOC Activities

Large language models (LLMs) and AI agents are having a significant impact in many fields, including cybersecurity. The sky’s the limit when it comes to their potential. However, down on Earth where LLMs are integrated into core workflows where business decisions are made, many issues, particularly around privacy and data accuracy, have emerged. We are left asking: Are AI agents trustworthy enough for my business?

When it comes to cybersecurity and Security Operations Centers (SOCs), the quick answer, like we’ve heard all our careers is: Yes, with proper controls and mitigations in SOCs, AI agents can be successfully and securely deployed in production environments where they add significant value working alongside human SOC analysts.

AI Cybersecurity Challenges

Off-the-shelf LLMs used as a standalone solution have several core problems, such as hallucinations, data poisoning, and prompt injection. These issues can cause serious problems in an autonomous SOC, including:

Limited Data Causing Inaccurate Verdicts – AI agents need all the relevant information from the environment to do their job well – at least as much data as human security analysts have, and ideally more. Insufficient data leads AI agents to make false assumptions that can vary across investigation runs. If you limit data access due to security concerns or constrain how much the AI can do for budget reasons, expect accuracy to take a hit.

Inconsistent Verdicts – AI agents must make decisions and carry out tasks based on the data they have collected. Due to the voluminous amount of security data in typical environments, it is common to take a sampling approach to balance accuracy with budget. When investigations are run multiple times, differences may appear in the verdict, its determined severity, and the recommended actions. This is normal as long as the difference is within a tolerance limit, and it happens even when two different human analysts look at the same alert.

Opaque Reasoning, Or The “Black Box” Problem – Some AI agents operate as opaque systems. These AI-driven SOC results can look very impressive at first, but for important business decisions you need to understand what led the AI SOC to make its recommended actions. This is, however, not an inherent limitation of AI, as some AI agents go to great lengths to be transparent. It is recommended that you validate AI agents thoroughly in a Proof of Value before committing.

How AI in Cybersecurity AI has Improved

AI-driven cybersecurity approaches have made significant strides toward improvement since first being introduced. Consider the following:

Consensus using Sampling Mitigates Inconsistency – Inconsistencies in decisions or outcomes can be effectively remediated by leveraging multiple AI agents with different configurations (e.g., different models at different temperatures) to interpret the data collected by prior agent operations.

Using sampling lets organizations understand where the different AI models align and where they diverge. As a result, relying on the information where all models agree and discounting the information where they differ can substantially mitigate inconsistency.

It’s important to note, however, that information where sample agents disagree are also valuable since they identify uncertainty and the need for better data or input. This inconsistent information can help organizations prioritize access to essential data for improved decision-making.

Consistent Procedures with Investigation Playbooks – One of the main reasons multiple runs of an AI SOC investigation result in inconsistent outcomes is the variation in not just the data collected, but also in the hypotheses created or modified during the AI agent investigation. Establishing a high-level standard operating procedure (SOP) investigation guide for certain categories helps agents form more consistent hypotheses and improves overall outcome consistency. This is not a novel approach – most human SOC analysts already rely on SOPs to ensure effective and consistent investigations. AI agents can use SOPs the same way.

Traceable Evidence Demystifies the Black Box – As a best practice, an AI SOC should be designed from the ground up with supporting evidence in mind. Every decision and hypothesis an agent makes must be backed by supporting information, including reasoning traces and the raw log data that substantiates that reasoning.

AI for Cybersecurity that’s Trustworthy

AI agents can accelerate detection, triage, and threat response in cybersecurity, but they also introduce real risks – including inconsistent outcomes, opaque decisions, and sensitivity to data quality. Trust for use in mission-critical environments requires evidence-backed reasoning including traces and raw artifacts; structured SOPs to reduce variance; multi-agent sampling to separate consensus from uncertainty; and guardrails for data integrity, prompt injection, and step and budget limits.

All these critical fixes can be addressed with advanced LLM AI agent approaches designed to tackle the complexity of modern AI-powered security operations. For example, some advanced LLM approaches use advanced multi-agent sampling to let organizations utilize the collective intelligence of diverse AI models that collaborate to achieve consensus, minimize inconsistencies, and pinpoint areas of uncertainty. Its structured and clear investigation guides ensure that analysis steps follow best practices and standardized procedures, driving consistency and reliability in every operation.

End-to-end decision traceability results in every verdict, recommendation, and automated action being audited and understood to provide full transparency to security teams, all the while building trust with stakeholders. By integrating these key elements – and implementing robust controls for prompt data quality, safety, and operational guardrails – advanced LLM AI agent approaches enable SOCs to achieve outcomes that are not only reliable and transparent, but also production-ready for real-world environments and your most mission-critical activities.