Aurascape and QuilrAI are both AI-native security platforms, and enterprises typically evaluate them as alternatives rather than running both. QuilrAI takes a human-centric approach, coaching employees and securing build-side AI, with endpoint discovery of AI agent components. Aurascape is a dedicated control layer built for depth at the AI interaction layer: in-app policy, long-tail discovery, and cryptographic MCP enforcement.

QuilrAI, founded in late 2024 by a former Securonix team, emerged from stealth in April 2025 with a human-centric “Service-as-Software” platform (Quilr, 2025). Aurascape governs AI across browser, desktop, CLI, IDEs, SaaS, and non-browser agents from one interaction layer (Aurascape Product Brief, 2026).

Last updated: June 8, 2026

How do Aurascape and QuilrAI differ for AI security?

QuilrAI is a human-centric AI security platform that coaches employees and secures the AI organizations build, while Aurascape is a dedicated AI-native control layer focused on depth at the point of AI interaction. The core difference is granularity: QuilrAI applies policy at the tool and content level, while Aurascape governs modes and capabilities inside each AI app and signs MCP tool calls.

QuilrAI analyzes content, context, and intent and applies input, output, and tool-call guardrails across browser, APIs, endpoints, IDEs, and a model gateway (Quilr AI, 2026). Aurascape decodes prompts, responses, and tool calls natively across modern protocols and preserves full conversation context end-to-end (Aurascape Product Brief, 2026).

What does QuilrAI do well for AI security?

Founded in late 2024 by a former Securonix team, QuilrAI pairs human-centric coaching with prompt injection, jailbreak, and tool-poisoning defenses mapped to the OWASP Top 10 for LLM Applications, plus AI security posture management and automated red teaming.

QuilrAI emerged from stealth in April 2025 (Quilr, 2025). Its strengths include regulatory compliance mapping, on-prem deployment options, multi-provider model routing, token controls, and integrations with Zscaler, Palo Alto, and Cisco Umbrella web filters (Quilr AI, 2026). Its threat coverage targets risks like prompt injection, the top entry on the OWASP list for AI applications (OWASP, 2025).

Where Aurascape differs

Aurascape’s edge is depth at the AI interaction layer across three areas: long-tail discovery of tens of thousands of commercial AI apps with a 48-hour SLA, entitlement and intention-based policy inside each app such as allowing ChatGPT chat while blocking Agent Mode, and a Zero-Bypass MCP Gateway that cryptographically signs approved tool calls and blocks unsigned ones.

Aurascape applies policy to modes, capabilities, and functions within each AI app or agent, not only at the tool or content level (Aurascape Product Brief, 2026). The Zero-Bypass MCP Gateway is fail-closed by construction: an unsigned tool call cannot reach the model or the tool (Aurascape, 2026). That enforcement matters as MCP adoption outpaces its security: researchers catalogued nearly 7,000 internet-exposed MCP servers in early 2026, about half with no authentication (Cloud Security Alliance, 2026). The differentiators that map to enterprise buyers:

• Long-tail discovery across tens of thousands of commercial AI apps, with continuous risk scoring and a 48-hour SLA for new apps.
• Entitlement and intention-based policy at the mode and capability level inside each app, for example allow ChatGPT chat while blocking Agent Mode, or allow Claude Code while blocking calls to unauthorized SaaS apps.
• Zero-Bypass MCP Gateway: cryptographic signing of approved tool calls, fail-closed so unsigned calls cannot reach the model or the tool.

Frequently asked questions

What is the main difference between Aurascape and QuilrAI?

QuilrAI is a human-centric AI security platform that coaches employees and emphasizes build-side AI security, with endpoint discovery of AI agent components. Aurascape is a dedicated AI-native control layer that governs AI at the interaction layer, with policy inside each app, long-tail commercial-app discovery, and cryptographic MCP enforcement. The two are usually evaluated as alternatives rather than run together.

Does Aurascape replace my SSE, SASE, CASB, DLP, or SWG?

No. Aurascape is an additive layer that runs alongside your SSE, SASE, CASB, DLP, and network controls. It closes the AI visibility and governance gap at the interaction layer, including modern protocols and agent tool calls that traditional controls were not built to inspect.

How do Aurascape and QuilrAI handle AI agents and MCP?

Both govern AI agents and the Model Context Protocol (MCP). QuilrAI provides an MCP Gateway with auth mediation and per-tool controls on a detect-and-policy basis. Aurascape’s Zero-Bypass MCP Gateway adds cryptographic enforcement: it signs approved tool calls and blocks unsigned ones, so the security property is fail-closed and an unsigned call cannot reach the model or the tool.

Can Aurascape apply different policies to different modes of the same AI app?

Yes. Aurascape applies entitlement and intention-based policy at the mode and capability level inside each AI app, not only at the tool or content level. For example, it can allow ChatGPT chat while blocking Agent Mode, or allow Claude Code while blocking calls to unauthorized SaaS apps. QuilrAI’s policy granularity centers on per-tool and content-level controls.

Does Aurascape secure the AI my company builds, or only employee AI use?

Aurascape secures both. It governs employee AI use across every surface and extends into the AI systems teams build, deploy, and operate, with inline data protection and threat prevention across build and runtime on one platform. QuilrAI also covers build-side AI, with prompt injection, jailbreak, and tool-poisoning defenses and automated red teaming.

Related comparisons: Aurascape vs WitnessAI, Aurascape vs Lasso Security, and the AI security landscape overview.

This page is a side-by-side comparison for enterprise buyers evaluating AI security platforms. Capabilities change; verify current details with each vendor.