Recce · Model Operating & Security Platform

The Security
Operating Layer
for AI.

Secure every model, dataset, prompt, API and agent across the AI lifecycle — from intake to production runtime.

Band A · Model Supply Chain
Band B · Data Ingestion
Band C · AI Guardrails
Band D · Telemetry
Band A · Model Supply Chain live
Recce@security-plane › 
ALLOW
The AI Security Problem

AI introduces security surfaces everywhere.

Every stage of the AI lifecycle exposes a distinct attack vector. Addressing them with isolated point tools leaves coverage gaps and destroys operational coherence.

Model
Malicious artifact CVE / backdoor Provenance gap
Data
PII leakage Malware Data poisoning
Prompt
Injection Jailbreak Extraction
API
Abuse Unauth access Rate bypass
Agent
Tool abuse Privilege escal. Chain attack
Runtime
Drift Exfiltration Policy violation
The Recce Answer

One security layer across the AI lifecycle.

Recce sits across every path into a running model — not inside a single component. One policy plane. One audit trail. Complete coverage.

Model
CVEAIBOM
Data
PIIMalware
Prompt
InjectionJailbreak
Recce
API
PolicyAuth
Agent
Tool ACLScope
Runtime
AuditSIEM
Platform

One platform. Four security domains.

Recce organises AI security across four discrete bands, each targeting a distinct attack surface with purpose-built controls.

Band AModel Supply Chain

Model Supply Chain

End-to-end pipeline governance — from model ingestion and CVE scanning to AIBOM generation, cryptographic signing, and OPA promotion gating.

Model ingestion pipeline
CVE / vulnerability scanning
AIBOM generation
Cryptographic signing (Cosign)
OPA promotion gate
Explore Band A →
Band BData Ingestion Security

Data Ingestion Security

Secure RAG pipelines and document ingestion against malware, PII leakage, and unsigned data assets before they reach the vector database.

RAG document ingestion
Malware scanning (ClamAV)
PII detection & redaction
Data asset signing
Vector DB security gate
Explore Band B →
Band CAI Guardrails

AI Guardrails

Responsible AI policy enforcement across the full stack — from data and model layers to prompt interfaces, APIs, and runtime execution.

NeMo Guardrails
OPA policy engine
Garak red-team coverage
Prompt injection detection
Runtime policy enforcement
Explore Band C →
Band DSecurity Telemetry

Security Telemetry

Continuous audit telemetry, model drift monitoring, and SIEM-ready export — giving SOC teams real-time visibility into AI security posture.

Security event streaming
Model drift (Evidently AI)
SIEM / SOC export
Immutable audit log
Compliance scorecard
Explore Band D →
Recce Security Fabric

One fabric. Every AI workload.

Four intake bands, one policy plane, one audit trail. Every deployment target inherits the same controls and writes back to the same telemetry stream.

Band A · Model Supply Chain Scan · AIBOM · Sign · Register OPA promotion gate Band B · Data Ingestion Malware · PII · Sign · Vector gate Milvus — clean content only Band C · Prompts & APIs OPA · NeMo · Garak · Runtime Allow / block per call Band D · Observability Audit · Drift · SIEM · SOC Splunk · Sentinel · QRadar · ELK Recce Security Fabric Centralized policy & governance Identity & access control Audit & compliance Encryption & signing Integrated workflows Deployment targets Model serving Applications APIs & services Agentic systems Edge / on-prem telemetry · audit · policy violations → Band D → SIEM / SOC
Band A · model supply chain
Band B · data ingestion
Band C · prompts & agents
Band D · observability
Recce policy plane
Explore Security Fabric →
Why Recce

Designed for how AI security actually works.

Five principles that distinguish Recce from isolated AI security point tools.

Lifecycle Security

Security controls follow AI from intake to runtime. No stage is unguarded.

Policy Driven

Centralized policies determine what can enter, deploy and execute. No manual gates.

Vendor Agnostic

Recce sits above the AI infrastructure layer. Swap the platform; controls remain.

Continuous Visibility

Every policy decision becomes security telemetry. Nothing is silent.

Enterprise Ready

Designed to integrate with existing SOC ecosystems, SIEM platforms and compliance frameworks.

AI Security Lifecycle

Security across the complete AI lifecycle.

From model creation to production runtime, Recce applies controls at every stage — nothing serves that hasn't cleared its gate.

Build
01
Band AProvenanceAIBOM
Ingest
02
Band BMalwarePII scan
Test
03
Band CAdversarialPolicy
Deploy
04
Band ACosignOPA gate
Run
05
Band CGuardrailsAPI ctrl
Monitor
06
Band DAuditSIEM
View full lifecycle →
Where Recce Fits

One security layer, whichever platform you run.

Swap the operating system, the Kubernetes distribution, even the hardware vendor — Recce is the constant that secures the column.

Recce
Security
Layer
AI Workloads

Generative AI · RAG · Notebooks · Agents

Served, retrieved from, experimented on and orchestrated as agents.

NVIDIA NIMTritonKServeMilvus
Platform · pick any

Whichever Kubernetes or AI platform you run

Recce is control-plane agnostic — the same gates apply regardless of underlying infrastructure.

SUSERancherRafayRed Hat OpenShiftNVIDIA Token Factory
Infrastructure

Hardware — GPU compute & isolated storage

GPU node pools, in-jurisdiction KMS/HSM, isolated landing, quarantine and clean-storage tiers.

GPU nodesKMS / HSMIsolated storage
Get Started

Secure your AI before it serves.

Schedule a technical review with the Recce team. We'll walk through your AI architecture and show you exactly where controls apply — and where gaps remain.