RSAC on LLM Consistency and Cybersecurity Trust

As generative AI becomes embedded in cybersecurity tools, trust is no longer an abstract concern—it is an operational requirement. 

New analysis shared by RSAC highlights a growing issue for security teams, MSPs, and IT resellers: the metrics used to measure large language model (LLM) consistency often fail to reflect how humans actually perceive reliability.

Consistency, defined here as whether a model produces the same output for the same input, has emerged as one of the most common signals for gauging AI trustworthiness. 

But RSAC’s latest research suggests that today’s consistency metrics are an imperfect proxy, particularly in high-stakes security environments.

What RSAC research reveals about consistency gaps

The RSAC analysis draws on a study of nearly 3,000 human evaluators comparing human judgments of consistency with widely used automated metrics. The finding is straightforward but concerning: technical measures frequently diverge from human perception.

In practice, this means an AI system may appear stable according to a metric while behaving unpredictably to a user, or be flagged as inconsistent even when humans see no issue. 

For security teams that rely on AI-driven alert triage, content analysis, or incident classification, this gap can create blind spots in their risk assessment.

The researchers propose a logit-based ensemble method that better aligns with human ratings than existing metrics, but they emphasize this as an incremental improvement rather than a definitive solution. The broader takeaway is caution, not replacement.

Read more: our team breaks down the latest trends in AI impacting channel professionals in 2026.

Cybersecurity doesn’t run on inconsistency

In creative or exploratory use cases, variability can be acceptable or even desirable. Cybersecurity is, of course, different. Inconsistent outputs can result in missed threats, false positives, or uneven enforcement of policies—outcomes that directly affect customer risk.

That is why consistency metrics have gained traction across security workflows. They are relatively easy to deploy, require no changes to the underlying model, and can serve as early warning signals for hallucinations, jailbreak attempts, or unstable reasoning.

As CrewAI CEO Joao Moura told us in December, trust is core to how people approach AI usage.

“AI agents are only truly autonomous when human trust is built into their core. The biggest barrier to adoption right now isn’t capability, it’s production-grade confidence,” said Moura. “To earn that confidence, we need a mature ecosystem with agent operation principles, compliance frameworks, and runtime transparency. Smarter models won’t make agents more reliable; trustworthy systems will. The future belongs to AI we can understand, measure, and hold accountable.”

RSAC researchers note that industry guidance is already beginning to reference consistency monitoring, even though standards for implementation remain loosely defined.

Why RSAC says consistency metrics aren’t enough

A key message from the RSAC analysis is that consistency metrics should be treated as part of a broader observability strategy rather than as a standalone trust signal.

The comparison to site reliability engineering is intentional. Just as SRE teams monitor latency, error rates, and system health without assuming perfection, AI-powered security systems require continuous measurement and calibration. 

Consistency can function as a live signal, but only when paired with human oversight and complementary metrics.

This is just the latest example of a concept long understood by many in security and IT operations: the overreliance on any single measure risks creating a false sense of confidence. In an AI-driven future, this might become more important than ever to remember.

It’s also important to keep foundational security needs in mind as teams leverage evolving technologies. When we spoke to SonicWall’s Michael Crean in September, he emphasized the importance of human error in many security incidents.

“Maybe the future for me is having more automation that is able to tell when the human being makes an error and doesn’t provision, install, implement something correctly, or turn something off and forgets to turn it back on. That’s still happening,” said Crean.

What this means for MSPs and IT resellers

For MSPs and IT resellers delivering AI-enabled security services, the implications are practical. Customers will increasingly ask not just what AI is doing, but how its behavior is monitored and controlled.

RSAC researchers recommend instrumenting AI pipelines to collect multiple outputs at each decision point, track consistency alongside latency and user satisfaction, and define thresholds that trigger escalation to human analysts. 

Regular calibration against human judgment is also critical, particularly after model updates or when expanding into new domains. 

As we’ve covered throughout the past year, human oversight and involvement remain critical even as AI tooling begins to manage tasks throughout security operations workflows.