Our Philosophy: The P‑TCA‑P Framework
At the heart of korvai.app is a simple idea: every decision an executive needs to make can be broken down into five universal elements. We call this the P‑TCA‑P framework. It is the grammar of our platform—the structure that transforms raw security data into board‑room clarity.
The Five Elements
P₁ – Tool Perspective
What the system observes. Every product has a bounded view – email, endpoints, cameras, networks.
Harmony sees email threats. CrowdStrike sees endpoint behaviour. Rhombus sees physical access.
P₂ – Viewer Perspective
Who reads the output. A CEO, CISO, COO, or franchise owner each need different language and priorities.
The same data becomes a one‑page board summary or a location‑level accountability report.
T – Trigger
What fires the analysis. An event, a threshold, a schedule.
A phishing email detected. A compliance score dropping below 70%. Monday morning at 6 AM.
C – Condition / Control
The rule or NIST standard that governs whether the trigger demands action.
NIST DE.CM‑01 (continuous monitoring) or a client‑defined policy threshold.
A – Action
The precise output. A verdict, a score delta, a report section, an escalation.
"TRUE_POSITIVE – escalate to CISO." "Endpoint score –15 points." "Executive summary generated."
Why P‑TCA‑P Matters
Most security platforms simply collect and display data. They leave the sense‑making to you. P‑TCA‑P is different. It is baked into every layer of korvai.app:
InaiyAi
routes every incoming event using the framework.
YaamAi
classifies threats with it.
KorvAi
scores your posture with it.
ParvAi
correlates cross‑product events using the framework.
TeymAi
monitors compliance health with it.
NadAi
writes every sentence of your report with it.
Every decision
is stored in an immutable audit trail — so you can always trace why a score changed or an alert fired.
Ethical AI by Design
P‑TCA‑P isn't just about security—it's about building AI you can trust. The five elements encode the core principles of responsible AI:
| P‑TCA‑P Element | Ethical AI Principle | How KorvAi Implements It |
|---|---|---|
| P₁ – Tool Perspective | Transparency | We document every data source and its limitations (e.g., "CrowdStrike sees endpoint behaviour, not network traffic"). |
| P₂ – Viewer Perspective | Accountability | Every report is tailored to its audience—a CEO sees different information than a CISO, because their ethical responsibilities differ. |
| T – Trigger | Explainability | Every decision starts from a traceable trigger—you can always answer "why did this happen now?" |
| C – Condition / Control | Fairness | Conditions encode ethical thresholds (e.g., disparate impact checks, regulatory compliance rules). |
| A – Action | Auditability | The decision_log stores every action with its full P‑TCA‑P context—a complete chain of custody for AI decisions. |
Built on emerging global standards
Built on Global Standards
Every condition in P‑TCA‑P is grounded in NIST CSF 2.0, the global benchmark for cybersecurity. When we say your Identity score is low, we can tell you exactly which NIST control is failing.
GV
Govern
ID
Identify
PR
Protect
DE
Detect
RS
Respond
RC
Recover
The Result
P‑TCA‑P turns complexity into clarity. It is the reason you get one email, not a dashboard. It is why your weekly report is actionable, not overwhelming. And it is the foundation of every intelligence korvai.app delivers.
The Decision Flow
↻ daily cycle
korvai.app AI Agents
InaiyAi
Ingest · validate · route
Normalises events from all products, validates signatures, routes to correct agent.
KorvAi
Orchestrate · score
6‑dimension posture score · weighted composite · board‑ready number.
YaamAi
Classify · suppress
TRUE_POSITIVE vs FALSE_POSITIVE · hard rules + LLM · confidence threshold.
ParvAi
Correlate · converge
Cross‑product correlations (physical+cyber) · CORR‑1 to CORR‑5.
NadAi
Narrate · RAG
Executive report generation · retrieves NIST · section‑by‑section.
TeymAi
Health · compliance
Monitors what's missing: training, MFA, patches, coverage – per location.
A Self‑Improving System
Like Andrew Ng's Context Hub, P‑TCA‑P is designed to get smarter with every decision. Every decision_log entry becomes training data for future iterations:
| KorvAi Element | Context Hub Parallel | How It Improves |
|---|---|---|
| Annotations | chub annotate | Agents flag edge cases (e.g., "Harmony webhook timestamp occasionally missing") – stored in decision_log. |
| Feedback Loop | chub feedback | Client‑facing thumbs‑up/down on report recommendations refines future outputs. |
| Versioned Knowledge | Curated docs in markdown | NIST controls, product APIs, and Azuris services are maintained as versioned knowledge in our RAG database. |
Every week, NadAi's reports get more accurate because the framework learns from what worked—and what didn't.
Continuous Improvement in Action
P‑TCA‑P Decision
event fires
decision_log
every action stored
Agent annotations
edge cases flagged
Improved docs & rules
weekly update
Next P‑TCA‑P Decision
smarter output
⬇️ Client feedback also refines reports ⬇️
How it works: Every decision is logged → agents annotate edge cases → docs and rules update weekly → next week's reports are smarter.
Grounded in NIST CSF 2.0 (including Recover function)and aligned with NIST AI RMF 1.0. Powered by korvai.app · A self‑improving intelligence platform.