All-in-One AI Security Platform
Enable Secure
Agentic AI Transformation.
A context & intent aware control plane to securely use, build and deploy agents across Endpoint, Cloud, Browser and embedded in SaaS Applications.
Trusted by
AI security is a runtime
action governance problem
Probabilistic by nature
LLMs output varies based on prompts. Every response is a new risk surface — you can't firewall a conversation.
Machine-speed decisions
Agents act on input prompts instantly, no human in the loop. Governance must be automated to match.
Data
What is being accessed
Identity
Who or what is acting
Actions
What is being done
Delegation
Who delegates what
Runtime authentication
Based on prompt, output, policies, and input validation — every next best action gets authenticated and authorized.
Delegation chains
Agents delegate tasks to sub-agents, creating layered permission hierarchies. Every hop is a new authorization decision.
See Agent in Action
Existing controls each capture
a fragment. None see the full chain.
The problem isn't that your tools are bad. It's that none of them were built to preserve the semantic chain from delegated prompt to action to outcome.
Identity Okta | EDR CrowdStrike | DLP Varonis | Network / Gateway Palo Alto | QuilrAI Guardian | |
|---|---|---|---|---|---|
Step 01 Prompt delegated Developer instructs agent via Slack to summarize contracts and email the team. | loses context Sees the user login only. | loses context No prompt visibility. | loses context Nothing yet. | loses context Nothing yet. | full chain Captures the prompt, user identity, intent classification, and endpoint context. |
Step 02 Context loaded Agent reads contract files, CRM records, customer PII, and employee directory. | loses context No local context. | loses context File reads — no meaning. | loses context Partial scan on disk, no agent context. | loses context Still nothing. | full chain Shows which files were accessed, why they mattered, and what PII was in scope. |
Step 03 Tool calls made Agent calls CRM API, queries customer DB, and drafts email with attachments. | loses context Service identity only. | loses context Process + file telemetry. | loses context No visibility into API data. | loses context Sees the API request only. | full chain Ties every tool call back to the original prompt, user, and data scope. |
Step 04 Email sent externally Agent sends contract summary and customer data to an external email recipient. | loses context Nothing. | loses context Connection metadata. | loses context Flags email after the fact. | loses context Outbound traffic only. | full chain Connects the email, PII included, intent, and original prompt — one full trace. |
Agentic architecture: from copilot to autonomous
01
Assistant
User → prompt → LLM response
Semi-auto02
Task Agent
Agent executes discrete tasks
Semi-auto03
Workflow Agent
Agent → browser, MCP, skills
Autonomous04
AI Employee
Agents orchestrate agents, full decisions
Full autonomyAI threats are not a feature for existing vendors to add. They require a net new platform that governs identity, data, endpoint, and network simultaneously — because agents cross all four in every interaction.
Security by design and at runtime
Monitor, Test and Protect AI from Endpoint to Cloud using a single decision engine.
Red Teaming
Employee AI Usage
Cloud Agents & AI Apps
Embedded SaaS Apps
QuilrAI
Decision Engine
Sub-50ms · Context · Intent · Trust
Proactive — by design
- 01Visibility + AI inventory — Every agent, skill, MCP server, tool, connection
- 02App configuration + guardrails — System prompt analysis, scope definition
- 03Identity + zero trust — NHID, delegation chains, least privilege
- 04Data + tool + MCP controls — Classification, lineage, access policies
- 05Agentic Red Teaming — Autonomous, continuous red teaming
- 06Custom Guardian Agents — Per-app security layer from intent analysis
- 07Governance + compliance — SOC 2, HIPAA, PCI, NIST AI RMF
Runtime — in production
- 01Prompt injection defense — Direct, indirect, tool-output injection
- 02Data access enforcement — Real-time DLP, redaction, classification
- 03Identity auth + authorization — Continuous verification, scope enforcement
- 04Guardrails enforcement — Intent alignment, instruction-action checks
- 05Two-way agent communication — Teach, don't block. Agent learns policy.
- 06Observability + debugging — Full chain tracing, reasoning audit trail
Coaching
Our Approach
Every Agent Gets a Guardian
QuilrAI finds every AI agent in your environment, reads its system prompt, assigns a dedicated Guardian Agent, and keeps testing and tightening its controls.
AgentPer-Agent · Autonomous
AgentPer-Agent · Autonomous
Key Differentiators
Endpoint Agentic Security
No other AI security platform monitors and enforces policy on Claude Code, Cursor, and Copilot natively on the machine. API gateways are blind to what happens locally. We're not.
Experience QuilrAI in Action
See how QuilrAI protects every AI surface, from coding agents and enterprise copilots to self-hosted models and custom-built AI apps.
Initializing scenario…
Scenario: Agentic Coding · Claude Code — Coding Agent
4
Solutions Protected
<50ms
Decision Latency
90%+
Auto-Resolved
0
Business Disruption
Simulated scenarios. Guardian Agent operates inline with zero latency impact
What Security Leaders Say
“Mitigating human-related risks is challenging. 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 · Securonix
“Security naturally creates tension. Quilr's approach uses AI to turn employees into active participants, promoting shared responsibility and reducing friction.”
Benjamin Godard
Head of Security · Spotnana
“QuilrAI lets us roll out AI without trading speed for PHI risk. It sits where our staff work and decides in real time what's safe.”
VP of Security
VP of Security · Sentara Healthcare
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