Agentic AI triages at machine speed. The bottleneck is now context — and agents reasoning over low-confidence feeds just reach faster shallow conclusions.
Agents without Kyberis may be fast — but confidently inaccurate. Agents with Kyberis know what they don't know.
The raw volume of threat data has outgrown any human team's capacity to review it — and it climbs every year. The counters below model that flood live: this is the bottleneck in real numbers.
Every counter below is a live model, not a cached snapshot — it recalculates the moment you load this page, using published annual rates projected forward to right now. The industry's own attempt to standardize threat intel for machines, STIX/TAXII, has seen fragmented adoption — most of it still moves as PDFs, spreadsheets, and email.
New CVEs published, 2026 (est.)
0
New malicious packages identified, 2026 (est.)
0
Mean time to exploit, 2026 (est.)
−7 days
Attackers now weaponize a vulnerability, on average, before it's even publicly disclosed. Patch-cycle defense can't win a race that starts after the exploit already has.
Sources — CVE volume: MITRE/CVE.org, 2025 annual total (48,174 CVEs, ~131/day). Malicious packages: Sonatype 2025 State of the Software Supply Chain (192,742 new malicious packages, ~528/day). Mean time to exploit: Mandiant M-Trends 2026, trended against 2018 and 2024 figures from prior M-Trends reports. Structured-intel adoption: industry/academic literature on STIX/TAXII uptake. Volume figures are modeled projections from 2025's published full-year data, not a real-time feed.
Human review can't scale to meet it, so triage moves to agents at machine speed. But an agent is only as good as the intelligence it reasons over — feed it raw, unscored signal and it just reaches a shallow conclusion faster. That's the real bottleneck Kyberis fixes: not the speed of analysis, but the trustworthiness of what your agents act on.
Where Kyberis is different — and why it matters when your agents are making decisions at machine speed.
Every indicator linked to actors, campaigns, and infrastructure. Confidence scores calculated from proof-point count and graph links — deterministic context for known-bad, probabilistic scoring for the fuzzy calls agents make at machine speed. Grounded in proof points, not LLM inference.
Every query deepens the context. The more Kyberis is used, the richer the graph becomes. That compounding data advantage is something a competitor launching today cannot easily achieve.
An MCP server at launch. Claude, Codex, Cursor, LangChain, Windsurf — any agent calling Kyberis over MCP acts before the threat window closes. Any agent, any framework, zero glue code.
Set up your agent →Every response carries source attribution, confidence scoring, and a full chain of evidence. Built for the analyst, the agent, and the auditor. It isn't an audit feature — it's the architecture.
Legacy threat intelligence was designed to be read by people. Kyberis is a specialized AI agent — intelligence designed to be called, reasoned over, and acted on by other agents at machine speed.
| Legacy threat intelligence | Kyberis | |
|---|---|---|
| Built for | Human analysts reading reports | AI agents — and the analysts who direct them |
| How it's consumed | Opened and read by people | Called over MCP / API by agents |
| Output | PDFs, dashboards, flat indicator files | Confidence-scored knowledge graph |
| Confidence scoring | Binary or none | Proof-point + graph-derived, every response |
| Evidence & provenance | Partial or shallow | Full chain of evidence, every response |
| Gets better with use | Static dataset | Compounds — deepens with every query |
| Time to value | Months of procurement | Your agent answering in minutes |
The result: confident decisions in 1–3 calls — each one scored, sourced, and defensible.
Start free and have your agent answering threat questions in minutes — or book an enterprise POV.
