The AI Buzz—and the Backlash

AI has become the new zero-trust: everyone claims to have it, few can prove it works.
When we recently sat down with two veteran CISOs from heavily regulated industries, the message was blunt: “If AI doesn’t save my analysts time or fit into the stack we already have, it’s just stickerware.”

The takeaway? Security leaders aren’t chasing algorithms. They’re chasing minutes—minutes saved in detection, investigation, and response.

Speed and Scale Define Value

In our conversation, both of the CISOs we spoke with used nearly the same phrase: speed and scale.
Tools that deliver real value shorten mean-time-to-detect (MTTD) and mean-time-to-respond (MTTR) while scaling to the growing volume of threats.

“Speed is our survival metric. If a tool adds complexity, it costs us speed—no matter how smart it claims to be.”

They emphasized that adoption friction is the silent killer. SOC teams often operate at “50–60 % strength.” Every new dashboard, every new UI, steals cognitive bandwidth.

The winning solutions: low-friction integrations that enrich existing SIEM or XDR pipelines rather than replace them.

AI’s True Job: Reduce Analyst Toil

The discussion revealed a subtle shift: AI in security isn’t about autonomy—it’s about assistance.
When AI can safely handle repetitive triage, contextual enrichment, or correlation, analysts are free to focus on decision-making.

Key use cases where AI adds measurable value:

1. Contextual enrichment: linking indicators, sandbox output, and threat intel automatically.
2. Dynamic prioritization: highlighting alerts that break normal behavioral patterns.
3. Analyst copilots: suggesting next investigative steps, backed by transparent logic.

Each use case depends on high-fidelity input. As one participant put it:

“Bad data makes brilliant AI useless.”

Trustworthy AI Needs Trustworthy Inputs

That’s where VMRay comes in. Our deep sandboxing and dynamic analysis generate the trustworthy signals that AI needs to make smart, explainable decisions.

Instead of feeding models vague alerts, VMRay enriches them with:

• Verified behavioral indicators
• Precise malware lineage and family insights
• Execution context and IOCs grounded in actual detonation evidence

This means AI triage models trained on VMRay data learn to distinguish real compromise patterns from noise—dramatically improving detection precision.

Integration Beats Innovation (Alone)

A recurring theme from the roundtable: innovation must fit, not fight.
CISOs prefer solutions that drop into established ecosystems—CrowdStrike, Palo Alto, Splunk, or Sentinel—without re-architecting their SOC.

VMRay’s API-first design aligns perfectly:

• Plug-and-play enrichment for SIEM/XDR pipelines
• Automated malware submission from email or EDR
• Instant verdicts that inform AI-driven decision logic

By focusing on interoperability, VMRay delivers speed (faster insights) and scale (handle more samples without manual review).

From Stickerware to Strategy

So how should CISOs evaluate “AI-powered” claims?

1. Ask for metrics: How much faster will we detect or respond?
2. Demand explainability: Can analysts verify why an AI made a call?
3. Check integration cost: Does it add or remove friction?
4. Validate data quality: Where does the AI’s context come from?

VMRay passes this test because it strengthens every downstream AI and automation layer—turning AI buzz into AI that buys you time.

Final Thought

AI in cybersecurity is only as valuable as the trust it earns in the SOC.
When detection accuracy, analyst efficiency, and integration speed improve together, CISOs finally get what they’ve been asking for: speed and scale without noise.