Security Models Built for Content, Context & Intent

Quilr’s research-driven models are engineered to safeguard data, AI interactions, and human workflows — delivering real-time protection with low latency, high accuracy, and explainable outcomes.

What Quilr’s Models Do

Quilr’s models are purpose-built to handle the modern threat landscape — from data leakage in SaaS and email to AI prompt injection, agent misuse, and human error.

  • Adaptive & Precise: Constantly learn from new threats, behaviors, and adversarial tactics.
  • Intent-Aware: Go beyond detecting what is happening to also capture why it’s happening.
  • Human-Centric: Integrate user feedback and Quilly’s coaching loops to reduce noise and increase trust.

Architecture & Technical Features

Hybrid Detection Engines

  • Combine transformer-based models with tree and graph classifiers.
  • Token-level and sequence weighting to surface low-signal threats (subtle prompt injections, insider misuse).
  • Adversarial training sets including OWASP LLM Top 10 and MITRE ATLAS scenarios.

Real-Time, Low-Latency Inference

  • Optimized for <100ms decision times across proxy, endpoint, and browser.
  • Lightweight edge models for IDEs, endpoints, and mobile clients.
  • High-throughput scaling in cloud, hybrid, and on-prem deployments.

Content-Context-Intent Guardrails

  • Content: Detect sensitive information like PII, PHI, IP, or secrets.
  • Context: Understand where it’s moving (email vs. Slack vs. Copilot).
  • Intent: Predict why the action is happening to avoid false positives.

Agent & AI Interaction Monitoring

  • Monitor and govern agent tool calls, decision-making, and external API access.
  • Block agent actions that exceed policy-defined intent or data sensitivity.
  • Audit every AI interaction for explainability and compliance.

Visibility & Auditability

  • Full decision logs with explainable AI justifications.
  • Native integrations with SIEM/SOAR platforms.
  • Compliance-ready reports for GDPR, HIPAA, NIST, EU AI Act.

Deployment & Performance

Browser & Endpoint Agents

Lightweight models embedded at the edge.

Proxy / LLM Gateway

Centralized enforcement with millisecond overhead.

SDK & APIs

Embed guardrails directly into custom enterprise applications.

Cloud, Hybrid, or On-Prem

Flexible deployment based on enterprise needs.

Performance is tuned for

Threats Quilr Models Defend Against

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Prompt injection, jailbreaks, and adversarial AI attacks

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Secrets and credential leaks in prompts, completions, or code.

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Sensitive data exposure in SaaS, email, and file shares.

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Rogue or misconfigured AI agents exceeding intended behavior.

Why Quilr

Content • Context • Intent

Understands what, where, why — then applies input/output/tool-call guardrails to prevent leaks and misuse in real time.

Unified DLP + AI Security

Unified DLP + AI Security
One platform securing files/email/SaaS and LLMs, copilots, and agents — with shared policies and logs.

Prompt-Injection & Jailbreak Defense

Built-in detection/sanitization for prompt injection, jailbreaks, tool hijacking, and adversarial patterns across every surface.

OWASP Top-10 (LLM) & NIST-Aligned

Controls mapped to OWASP Top-10 for LLM Applications, guided by NIST AI RMF and MITRE ATLAS techniques.

Human-Centric with Quilly

In-flow coaching that explains why something is risky and offers safer alternatives — fewer false blocks, faster resolution.

Everywhere Deployment, Enterprise Governance

Browser, APIs, Endpoints, IDEs, LLM Gateway/Proxy, SDK — with audit trails, data residency options, and compliance reporting.
WHAT PEOPLE ARE SAYING

“Quilr offers a novel approach using AI agents to solve an immediate, top-of-mind problem globally that resonates with enterprise CISOs in India, Southeast Asia, and the Middle East,” said Anuj Gupta, CEO of Hitachi Systems India. “We’re partnering with Quilr to help our customers drive innovation while enhancing security.”

Anuj Gupta - CEO of Hitachi Systems India

“Mitigating human-related risks is challenging,” added Tanuj Gulati, former CTO and founder of Securonix. “I believe in Quilr’s mission to transform employees into frontline defenders against cyberattacks rather than the weakest link.”

Tanuj Gulati - Former CTO and Founder of Securonix

“Enterprises haven’t changed their approach to security in years, which leaves proactive measures idle because of high disruption,” said Sachin Gupta, CISO at Protiviti. “Quilr uses AI to fill a significant gap—improving overall posture, saving time, and enabling secure AI adoption and a positive security culture. It’s truly transformative.”

Sachin Gupta - CISO at Protiviti

“Security naturally creates tension, and labeling people as weak links doesn’t help,” said Benjamin Godard, Head of Security at Spotnana. “Quilr’s approach uses AI to turn employees into active participants, promoting shared responsibility and reducing friction.”

Benjamin Godard - Head of Security at Spotnana

“Quilr is redefining how organizations manage user behavior. With most breaches caused by human error, it has the potential to be a real game changer in reducing employee-driven incidents and data leaks. What sets Quilr apart is its mindset: employees aren’t the problem—they’re the solution. By actively coaching users and spotting risky behavior early, Quilr helps people make smarter, safer decisions in the moment.”

James Kirk - Head of Security & privacy at Jellyfish
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