Top Costs of SOC Hallucinations
Artificial Intelligence (AI) is transforming security operations centers (SOCs) and detection & response teams. The need to quickly identify anomalies, combine disparate data sources, and prioritize alerts is placing AI at the forefront of SOC discussions. But there is something lurking which can cause undesirable outcomes, AI hallucinations.
What is an AI Hallucination?
In the words of the OWASP Top 10 for Large Language Model Applications (OWASP LLM), hallucinations occur when AI generates responses that seem plausible, but are entirely incorrect or misleading. For a SOC, this could result in misidentifying a benign event as malicious, misclassifying a critical threat, or fabricating event correlations. These hallucinations may result in real-world consequences, especially when it comes to the high-stakes security operations.
Why does a hallucination occur in the first place? At the core, LLMs are making predictions as to the next most likely token, the accuracy of that prediction is dependent on the patterns identified during training. How the model was trained, what data the model was trained on, etc., these aspects impact their frequency, triggers, and types of hallucinations that may occur. Google has an easy to understand writeup with more details.
Hallucinations in AI-driven SOCs can introduce risks. In a field where speed and precision are critical, these mistakes can damage trust in AI systems and cause analysts to second-guess automation, undermining its potential value. Despite these risks, we continue forward because the alternative, our current SecOps structure, comes with its own significant flaws.
Human analysts are drowning in alerts. The sheer volume often forces teams to apply aggressive filtering, which can result in missed threats. Manual investigations are time-consuming, error prone, and missed leads can quietly evolve into incidents. Meanwhile, analysts spend an overwhelming amount of time chasing false positives, burning out on tedious, low-value work. The system isn’t just inefficient, it’s unsustainable. That’s why we’re embracing AI: not because it’s perfect, but because the current approach isn’t improving. We’re betting on innovation – because sticking with the status quo guarantees we fall behind.
Stay tuned for the innovative details on how Embed uniquely approaches this problem.
What is the Cost of a Mistake?
1. Missing an Attack
When AI fails to detect a real threat, the cost can be substantial in exposure risk, financial risk and trust. A missed ransomware execution, lateral movement, or exfiltration can result in undiscovered breaches. The financial impact of such a failure can be measured in terms of lost revenue, regulatory fines, and damage to brand reputation.
2. False Prioritization
On the flip side, false positives and misclassifications introduce their own set of challenges. Every incorrect alert that requires investigation drains resources, pulling analysts away from real threats. Over time, this creates alert fatigue, leading to a failure to respond appropriately when a genuine threat does arise.
3. Inaccurate Recommendation
LLMs excel at generating recommendations because they can quickly synthesize vast amounts of information, identify patterns, and provide contextual guidance based on prior examples. However, the risk arises when an LLM hallucinates. This could mean suggesting a remediation step that isolates the wrong system, overlooks the real threat, or implements a change that weakens the environment.
4. Incomplete Investigation
Each alert is unique, varying in scope, tactics, and impact which makes AI agents particularly well-suited to adapt dynamically and tailor their approach to the specifics of each case. However, this flexibility comes with risk: if an agent hallucinates, it may follow an incorrect line of inquiry or omit crucial steps, leading to an incomplete investigation. This can result in missed root causes, residual attacker presence, or misinformed conclusions, ultimately compromising the integrity and effectiveness of the incident response process.
That sounds bad
At Embed, proper usage of AI and ML training and guardrails has produced good outcomes for our customers. We see improvements in attack identification, vastly improved alert prioritization, solid recommendations, and proper investigations that evolve with the threats and avoid unnecessary investigation.
We believe the solution isn’t human-only or AI-only, it’s a blended approach that combines the strengths of both. While AI can accelerate investigations and reduce alert fatigue, in some cases, human expertise remains essential for nuanced context, judgment, and oversight. We explored this balance in a previous blog, Is AI Replacing Security Analysts? Should We Find New Careers?, where we emphasized that the future of the SOC is collaborative acceleration between analysts and AI.
But not all AI SOCs are constructed in the same way.
The Trust Factor
If AI hallucinations degrade trust in automation, analysts may revert to manual processes, negating the efficiency gains AI was meant to provide. Striking the right balance between automation and accuracy is critical.
Trust is the foundation of any security technology. If AI routinely gets things wrong, security teams will be hesitant to rely on it for critical decisions. Establishing AI trust is an ongoing journey, one that requires transparency, continuous refinement, and rigorous validation of AI-driven insights. As we continue refining AI for security, trust and accuracy must remain at the forefront of innovation.
The journey toward trusted AI in security is just beginning. Stay tuned for our upcoming post on The AI Trust Journey, where we’ll deep dive into how to build confidence in AI-driven security solutions.