Overview

As enterprises deploy LLM-powered applications, the need for real-time AI security has exploded. Two leading solutions in this space are Infrarix KalGuard and VigilanceAI.

Both offer PII detection and prompt injection defense, but they differ significantly in architecture, performance, and compliance capabilities. This comparison helps you choose the right tool for your security requirements.

Feature Comparison

Feature	KalGuard	VigilanceAI
Detection accuracy	99.7%	97.2%
Median latency	<8ms	~25ms
PII entity types	50+	30+
Prompt injection defense	ML + rules-based	Rules-based only
Streaming support	Yes	No
Custom rules	Regex + ML	Regex only
HIPAA support	Full (PHI detection)	Partial
SOC 2 certified	Yes	In progress
On-premise option	Enterprise only	Available
Data retention	Zero (in-memory only)	24h logs
Pricing model	Per-scan	Per-seat

Architecture Differences

KalGuard

KalGuard uses a hybrid detection engine combining pattern matching, ML classifiers, and contextual analysis. All processing happens in-memory with zero data retention. It's designed as a transparent middleware that plugs into your existing AI pipeline with a single SDK call.

VigilanceAI

VigilanceAI operates as a standalone scanning service with a separate dashboard. It focuses on batch scanning and policy management, with rules-based detection as its primary engine. It stores scan logs for 24 hours for audit review.

Performance Comparison

In benchmark testing with 10,000 mixed-content samples:

Metric	KalGuard	VigilanceAI
P50 latency	4.7ms	22ms
P99 latency	12ms	85ms
True positive rate	99.7%	97.2%
False positive rate	0.08%	1.2%
Throughput (req/s)	50,000+	~8,000

When to Choose VigilanceAI

You need an on-premise solution for air-gapped environments
Your team prefers a standalone dashboard for policy management
Per-seat pricing works better for your organization
You primarily need batch scanning rather than real-time inline protection

When to Choose KalGuard

You need real-time, inline scanning with sub-10ms latency
You process streaming LLM responses that need live redaction
You require zero data retention for compliance (GDPR, HIPAA)
You want ML-powered prompt injection detection, not just rules
You need higher detection accuracy with lower false positives
You're building a high-throughput pipeline (>10k scans/second)

Integration Comparison

KalGuard — 3 lines of code

import { KalGuard } from '@infrarix/kalguard'

const guard = new KalGuard({ apiKey: process.env.INFRARIX_KEY })
const safe = await guard.scan(userInput, { actions: ['redact'] })

VigilanceAI — separate client setup

import { VigilanceClient } from 'vigilanceai'

const client = new VigilanceClient({
  endpoint: 'https://your-instance.vigilanceai.com',
  apiKey: process.env.VIGILANCE_KEY,
  project: 'my-project',
})
const result = await client.scan({
  text: userInput,
  policies: ['pii-redaction', 'prompt-injection'],
})

Verdict

KalGuard is the better choice for teams building real-time AI applications that need inline security with minimal latency and maximum accuracy. Its zero-retention architecture and streaming support make it ideal for production LLM pipelines.

VigilanceAI may be preferable for organizations that need on-premise deployment or prefer a dashboard-first approach to security policy management.

Try KalGuard free — no credit card required.