Klair Vuby Prak Knit
Security Risk Operating System (SROS)
Turns fragmented security data into business-contextualised risk intelligence.
Connecting security. Simplifying risk.
The Problem
Each team reports up. No one sees across. The complete picture exists — scattered across eight databases that have never been introduced to each other.
The Problem
Every enterprise runs 8–15 security tools. Thousands of findings. Zero business context.
The Proof
Severity scores treat these as identical. Context tells you they're worlds apart.
Introducing Klair Vu
Klair Vu does not replace your security tools. It makes their data work together — connected through business context, risk, and outcomes.
The Architecture · Where Klair Vu Sits
Without Klair Vu, every tool's report reaches the CISO separately — fragmented and without business context. Klair Vu is the layer that makes them one.
How It Works
Bring security data from every tool into a single platform. No rip-and-replace.
Common security data model. Deduplication across sources.
Link findings to assets, apps, business services, owners, criticality.
Severity × exploitability × exposure × business impact.
Route the right risk to the right team, with SLA and ownership attached.
MTTR, risk reduction, cost, and security effectiveness — tracked live.
Measurable Impact
| KPI | Typical improvement | Why |
|---|---|---|
| Incident investigation time | ↓ 50–70% | Cross-tool correlation and auto-triage |
| Mean Time to Respond (MTTR) | ↓ 40–60% | Context-enriched alerts end manual investigation cycles |
| Alert fatigue / false positives | ↓ up to 80% | Business-context scoring filters noise before analysts |
| Risk prioritisation effort | ↓ 60–75% | Automated risk scoring replaces CVSS-only triage |
| Compliance reporting time | ↓ 50–70% | Unified posture auto-generates audit-ready evidence |
| Analyst productivity | ↑ 2–4× | Analysts work real risk, not data aggregation |
| Cross-team blind spots | ↓ 70–90% | One pane across AppSec, Cloud, IAM, Endpoint & GRC |
Evidence-based industry benchmarks aligned to Klair Vu capabilities. Actual results vary by organisation size, tooling, and implementation depth.
What Makes It Defensible
Competitors sell one slice — VM, or compliance, or risk quantification. Klair Vu spans findings → app & business-unit mapping → attack surface → CTEM → formal risk register → compliance crosswalk, in a single tenant. For the buyer who can afford exactly one tool, that is the story.
"Ask Klair Vu" and narrative banners — "risk is rising, expect board questions" — for buyers whose boards don't read CVSS. Rivals show charts; Klair Vu writes sentences.
Everyone else sells to metro enterprises and funded startups. Nobody seriously sells a full security operating system — at a price a mid-market CFO can approve — to tier-II companies and co-operative banks. The uniqueness is who and how, as much as what.
SaaS, private cloud, on-premise, or fully air-gapped — genuinely rare at this price point, and decisive for banks, regulated entities and OT-adjacent buyers.
The Team · Prak Knit
Connecting security. Simplifying risk. · app.prakknit.com · 2026
End of Main Deck
The slides that follow are backup — depth on demand. Jump to them if a question calls for it: AI architecture & threat modeling for a technical audience, compliance crosswalk for a regulated buyer, the India landscape for competition, and the setup slides for a longer telling.
The Paradox
Data lives in many tools with limited integration and visibility.
Too many alerts, high false positives, low signal-to-noise.
Technical findings are hard to translate into business risk.
Incidents need hand-stitched investigation across domains.
Detecting after the incident — not predicting or preventing.
Manual reporting: slow, error-prone, always out of date.
EDR, SIEM, CSPM, IAM, GRC — every category has excellent products. What's missing is the layer that makes them mean something together.
The Insight
Klair Vu layers five dimensions of context onto every finding — transforming raw scanner output into prioritised, defensible decisions.
Does the same asset appear across multiple tools? Compounding signals raise real risk.
Tier-1 payment API vs internal wiki — same CVE, 10× different urgency.
Internet-facing with no WAF is a different world from internal-only.
Revenue, compliance scope, customer data — what does it actually touch?
Compensating controls or patches in flight reduce the true risk.
What Leadership Actually Asks
What actually puts the business at risk today?
What should be fixed first — and in what order?
How are the teams performing? Where are the bottlenecks?
Are critical risks being addressed on time?
Where is security spend actually going?
Are our security initiatives actually working?
AI Architecture
Three layers, one rule: the model proposes, the deterministic layer decides. Every AI feature works with zero external calls — degrade, never fake.
Risk scoring, correlation, threat modeling and Ask Klair Vu run on explainable, auditable logic. Fully functional air-gapped, out of the box.
Answers are assembled from real rows in the customer's own security graph, retrieved by embeddings computed inside our process. Nothing invented, nothing leaves.
Wording by an LLM only if the operator enables one: cloud models, or the customer's own — Ollama / any internal OpenAI-compatible endpoint. On-prem, the prompt never crosses the perimeter.
AI Architecture · Threat Modeling
From an architecture description to a reviewable threat model in minutes — grounded in a curated, framework-mapped threat library, so nothing rests on a model's imagination.
Plain-text architecture, a guided wizard (components → trust boundaries), or your existing app inventory. Diagram optional.
Components, technologies and context detected — gateways, queues, Kubernetes, data classes, internet exposure. Deterministic and explainable; an LLM extractor (cloud or your own) can accelerate it.
Candidates matched against a curated library — 115+ threats across 15 categories, each mapped to STRIDE, CWE, CAPEC, OWASP, MITRE ATT&CK, NIST 800-53 & ISO 27001.
Engineer reviews against a test-case checklist; accepted threats become tracked findings with owners and SLAs. Which model produced what — recorded on every threat model.
Compliance Intelligence
How a live compliance number stays clean and confident across many frameworks — without an army of assessors.
A machine-checkable control fails only when a live, open finding maps to it through its detection rules — and passes when none does. Every status traces to evidence.
Equivalent controls are linked across frameworks — NIST ↔ ISO ↔ CIS ↔ PCI — with confidence-weighted mappings. One finding updates every framework it touches; one fix can clear controls in seven frameworks at once.
Manual and auditor assessments are never overwritten by automation. Machine and human judgments are stored separately, with provenance on every row.
When only part of the estate is scanned, the dashboard says so — "results partial until coverage is complete" — instead of faking certainty. Scores recalculate continuously as findings open and close.
What Changes
Where Klair Vu Fits
"What assets do we have and what's their state?"
"Which vulnerabilities should we fix first?"
"What are our cloud risks and misconfigurations?"
"What events and alerts need investigation?"
Connects findings across every tool and domain into one view — prioritised by business context, routed to owners, measured to outcomes.
The India Landscape
| Klair Vu | Strobes | Seconize | SecPod | Scrut / Sprinto | SAFE | |
|---|---|---|---|---|---|---|
| Multi-tool findings unification | ✓ | ✓ | ✓ | ◐ | — | — |
| App / business-unit mapping | ✓ | ◐ | ◐ | — | — | ◐ |
| CTEM lifecycle | ✓ | ✓ | — | — | — | — |
| Formal risk register (ISO / NIST) | ✓ | — | — | — | ◐ | — |
| Compliance crosswalk | ✓ | — | ◐ | — | ✓ | — |
| Plain-English AI layer | ✓ | — | — | — | — | ◐ |
| On-prem / air-gapped | ✓ | — | — | ✓ | — | — |
| Tier-II mid-market pricing | ✓ | — | ◐ | — | — | — |
Assessment based on public product positioning, July 2026. ◐ = partial capability. Astra, Beagle & CERT-In-empanelled VAPT firms are channel partners, not competitors — they generate the findings Klair Vu operates on.