If you are evaluating an AI SOC solution, one critical question to ask is: what’s under the hood?
While every AI SOC vendor is touting their LLMs, even the best AI cannot deliver the highest accuracy, consistency, and ability to scale.
In this blog post we’ll examine the main approaches that can be used in AI SOC platforms: deterministic tools and LLMs. We also will explain where each approach excels, where each falls short and why it’s important that your vendor of choice uses both in triaging and investigating all your security alerts. We also will explain why vendor claims of using analytic forensic tools need examination, as the depth and capabilities can greatly vary.
Deterministic analysis: The guardrails for accuracy
Deterministic analysis is rule-based and repeatable. Given the same input, they always deliver the same output. In SOC triage, this means:
- High consistency: Alerts are handled the same way, every time.
- Auditability: Easy to retrace and explain why an alert was escalated or suppressed.
- False positive reduction: By encoding expert knowledge into rules, deterministic logic filters out noise at scale (Intezer for example achieves ~98% accuracy here).
- Fast, scalable and affordable: Deterministic analysis executes in real time with minimal compute overhead, allowing thousands of alerts to be triaged simultaneously without requiring loads of expensive compute infrastructure (with cost savings passed on to the customer).
Not all deterministic analysis packs the same punch
It’s important to note that not all claims of “deterministic capabilities” are created equal. It’s like comparing two doctors, one who uses only a thermometer and another who uses an MRI. Both tools are deterministic in the sense that they always produce the same result when applied. The thermometer will consistently show a temperature, and the MRI will consistently reveal detailed imaging, but the depth and richness of the diagnosis they provide are worlds apart.
So too in the SOC. Some vendors may claim deterministic capabilities because they scan a hash in VirusTotal, which is the equivalent of taking a patient’s temperature. Intezer, by contrast, delivers MRI-level depth with its deterministic tools. For example with our proprietary database of billions of malicious and benign code samples, Intezer can determine whether previously unknown code shares genetic fragments with malware families, delivering a much more thorough and conclusive verdict than surface-level checks ever could. Additional examples of Intezer’s deterministic tools can be endpoint forensics, memory scanning, URL scanning, threat intelligence and reputation checks for files/URLs/IPs, as well as specific org policies and tuning rules.
Where deterministic shines
Deterministic analysis excels in areas where repeatability, precision, and proven logic matter most. For example, matching file hashes to known malware databases delivers fast, definitive results. If a file has been identified as malicious before, there’s no ambiguity in the verdict. The same principle applies to enforcing allow or deny lists, where repeatable rules ensure consistent triage across every alert.
Furthermore, deterministic analysis tools are lightning fast and do not require massive amounts of computing power, making them more affordable to customers.
In short, deterministic methods ensure accuracy, consistency, clarity and affordability in triage, forming the guardrails SOC teams rely on to operate with confidence and efficiency.
Where deterministic falls short
While deterministic processes provide reliability and consistency, they have inherent limitations. Rules and signatures cannot adapt to new or unknown attack patterns and new triage strategies, leaving gaps when adversaries introduce novel techniques. Deterministic logic is also blind to subtle intent; for example, it may flag a PowerShell script as suspicious without understanding whether it is part of a malicious operation or simply a routine IT maintenance task. These blind spots highlight the need for a complementary layer of analysis that can reason more like a human analyst, interpreting context and intent rather than relying solely on fixed patterns.
Read about what CISOs are looking for in an AI SOC platform
LLMs: Human-like reasoning at scale
Large Language Models bring human-like reasoning into SOC triage, acting as an analyst’s intuition at machine speed. They are able to interpret ambiguous signals, connect seemingly unrelated events, and generate verdicts like those of an experienced human operator. This capability makes them particularly valuable in situations where rigid rules fall short.
Where LLMs excel
LLMs excel at interpreting context and intent, taking raw technical evidence and transforming it into clear, actionable verdicts. Unlike deterministic processes, which can parse data but struggle with meaning, LLMs provide the analyst’s intuition at scale.
Here are 3 examples of LLMs in action:
Endpoint use case: Deterministic engines can tell you that an unknown executable spawned cmd.exe and then invoked rundll32.exe on a DLL. LLMs takes it further, analyzing the full chain: a Temp-folder EXE immediately launching a suspicious DLL registration sequence, with no legitimate installer context, flagged as malicious by both the EXE and DLL verdicts. The LLM highlights that the entire process chain—Temp EXE → cmd.exe → rundll32.exe—is consistent with malware persistence, not routine user activity. This is the difference between a list of events and a reasoned conclusion.
Identity use case: Traditional rules may flag multiple rapid IP logins as a travel anomaly. LLMs enriches the context, recognizing that the IPs belong to known corporate VPN relays and Microsoft endpoints, that MFA was consistently passed, and that devices and geolocations match historical patterns. The LLM interprets these signals to conclude it is a benign VPN-driven false positive, rather than a compromised account.
Email use case: Deterministic checks can identify mismatched domains or suspicious links. But LLMs can tie the indicators together into a clear verdict: the sender address does not match the sender’s name, the URLs point to untrusted subdomains, branding is off, and an urgent payment request is out of place. Instead of a raw list of red flags, the LLM delivers a reasoned judgment that the message is a phishing attempt.
Across these examples, Intezer’s LLM doesn’t just parse or enrich, it interprets. By explaining whether activity is malicious, suspicious, or benign, it provides the intuition needed for fast and confident SOC decisions.
Where LLMs can fail
Despite these strengths, LLMs carry inherent limitations. They can be inconsistent, producing different answers to similar inputs. They are also prone to hallucinations, delivering conclusions with great confidence even when those conclusions are wrong. Perhaps most importantly, their reasoning is not inherently repeatable or auditable, which undermines trust in high-stakes SOC operations where explainability and accuracy are critical. Finally, relying solely on LLMs for alert triage, investigation and response is exorbitantly expensive with enormous needs for compute power. This often translates into vendor charging per alert which is simply not economically scalable.
Why “either/or” approaches miss the mark
In summary, vendors who rely on one or the other approach simply cannot deliver on the promise of the modern SOC.
- LLM-only platforms often market themselves as fully powered by AI and capable of handling any and all security use cases. While impressive on the surface, they can create risk if verdicts are inconsistent, opaque, or prone to hallucination. As mentioned the LLM-only approach is not economically scalable considering the massive volume of alerts that need to be triaged for most enterprises.
- Deterministic-only platforms provide reliable automation but lack the adaptability to interpret ambiguous signals.
Both approaches in isolation leave gaps. That is why a hybrid solution that incorporates both, is ideal for the modern SOC.
Intezer’s approach: Guardrails + intuition
Intezer was built on the belief that no single approach is enough to meet the demands of modern SOC operations. Deterministic analysis provides the guardrails with repeatable, explainable processes that reduce false positives by ~98% and deliver consistent accuracy at scale. This foundation is what makes the platform reliable, trustworthy, and audit-ready.
But deterministic processes alone are not sufficient. Attackers constantly evolve their techniques, and subtle differences in intent can determine whether an activity is routine IT maintenance or a genuine intrusion. This is where Intezer’s use of multiple Large Language Models adds balance. LLMs act as the analyst’s intuition, interpreting ambiguous signals, correlating weak indicators, and generating verdicts that mirror the reasoning of a seasoned security professional.
By combining deterministic rigor with LLM-driven reasoning, Intezer delivers verdicts that are both accurate and insightful. The deterministic layer ensures consistency and confidence, while the LLM layer brings flexibility, context, and adaptability. Together, they augment the work of human analysts, delivering machine speed and scale to the task of alert triage and response.
Read more about how Intezer follows Anthropic’s best practices.
Everything you need, out of the box
Intezer also eliminates the integration tax. With built-in threat intelligence, enrichment, and triage tooling, our platform connects directly to your existing alert sources (EDR, SIEM, cloud, and more). No additional stack or bolt-ons required. See all Intezer integrations.
Top-tier human analysis at scale
The AI SOC market is crowded, and not all “AI” carries the same weight. As you evaluate solutions, dig deeper than the buzzwords. Ask how the platform balances deterministic accuracy with AI flexibility, how it creates verdicts, and how it reduces, not adds to, the workload of your team.
Inquire about vendor pricing. Make sure it’s not based on the volume of alerts being triaged as this may force you to cherry-pick which alerts to ingest.
At Intezer, we believe that replicating human analysis at scale requires both the guardrails of determinism and the intuition of AI. Together, they deliver the SOC accuracy, speed, and efficiency that modern security teams can trust.
To learn more about Intezer and see how our AI SOC platform can help you focus on real threats, book a demo today!
Zev Schonberg
