Forward Deployment Engineer

Introducing Aquila. Alterion is excited to launch Aquila, an endpoint AI governance suite for agents running on your employees' devices. Asim husain. Co-Founder. August 17, 2026 Read article July 27, 2026 Insights from the openai x hugging face incident. Read article July 16, 2026 Introducing Draco. Read article When Alterion launched Draco, Alterion focused on the agents running in cloud and server-side environments. Since then, a second front has opened. AI coding agents, browser-based assistants, and locally running models now execute directly on enterprise-managed laptops (endpoints), where a growing share of enterprise AI activity happens and where almost none of it is governed. That is why Alterion is introducing Aquila, an endpoint AI governance suite that extends the same runtime visibility and enforcement Alterion built for cloud agents to endpoint devices. Aquila evaluates prompts, tool calls, and uploads on the endpoint itself, before they execute. Why Alterion built this. One of the biggest challenges with AI agents in enterprises is managing their growing presence across the organization, particularly on company devices. Engineers are using AI coding agents to build software. Employees are installing AI browser extensions on their work devices to help with everyday tasks. Across the organization, these autonomous agents are being connected to internal company data in order to execute end-to-end workflows. Now multiply these agents across multiple devices for each of tens of thousands of employees. It's hard to imagine just how many ungoverned agents exist across a modern enterprise ecosystem. When humans interact with AI, Alterion often let its guard down. Alterion send hundreds of prompts a day, drag files directly from company systems into chat windows, and click "Allow" on an agent's actions without thinking twice. That casual behavior can create serious security risks. 38% of workers share sensitive data with AI tools without their employer's knowledge, and 66% have used unauthorized AI at work. In those moments, it's easy to forget that the data flowing through these interactions can include source code, credentials, customer information, and regulated data. Meanwhile, the agent is executing shell commands, writing to local files, and pushing changes to remote repositories. Almost none of that activity is visible to traditional security tooling. That's what makes shadow AI on the endpoint one of the fastest-growing enterprise exposures. Its solution - three endpoint enforcement points. With Aquila, every prompt, action, and tool call is captured and evaluated on the device before it leaves the endpoint. Prompts are evaluated before they're submitted. Tool calls are evaluated before they're executed. If someone uploads a document containing source code, credentials, customer records, or other sensitive data, Aquila can inspect it and flag it before anything is transmitted. Built for speed, evaluations run locally in milliseconds. The inline policy engine is deterministic, so raw endpoint content never has to be sent to an LLM in the hot path, and enforcing action never depends on a network round trip. The platform also provides real-time visibility into agent behavior. Activity on the device is captured as sessions containing every prompt, tool invocation, and action, all attributed to both the user and the endpoint. Those sessions feed the same Risk Score and audit trail as cloud-based agents, so governance works across both environments through a single record. The suite is made up of three enforcement points: Aquila Code governs AI coding agents on the device. It evaluates tool calls before they execute, stopping destructive shell commands writes to protected paths or pushes to production branches. The goal is to decrease risk and increase efficiency by discovering them at the point of invocation instead of during code review. Aquila Browser governs AI browser extensions and web AI applications. It evaluates prompt text and upload metadata, protecting the place where source code, credentials, and regulated data most commonly leave managed devices. If an interaction violates a locally enforced policy - for example, by attempting to send customer PII in a chat - it is flagged for review before the data is transmitted. Aquila Local Gateway sits between local AI tools and their destinations. It sees where AI requests are headed and applies your organization's policies to desktop AI apps, locally hosted models, and on-machine MCP servers. What you gain with Aquila. With Aquila, you can: * Discover every AI tool in use across managed endpoints, including devices used by remote and work-from-home employees. * Prevent sensitive data exposure by stopping risky prompts and file uploads before they reach external AI models. * Block risky or destructive AI tool calls as they happen. * Track AI usage and costs by user, device, model, and application. Getting started. Aquila is available today for enterprise customers. If you are managing AI adoption across a large endpoint fleet, Alterion would love to show you what is possible.

Alterion has launched Draco, a runtime control platform for enterprise AI agents. The San Francisco-based company's platform provides real-time visibility and governance over AI agents operating across clouds, vendors, and endpoints without requiring code changes. Draco addresses a gap in enterprise AI deployment by sitting between AI agents and infrastructure as a dedicated runtime control layer. It observes every prompt and action in real time, models agent behaviour, and applies programmable guardrails to enforce policy before agents perform high-risk actions. The platform is designed for organisations in highly regulated environments, helping them comply with frameworks such as SOC 2, ISO 42001, and the EU AI Act. Alterion was founded by Alharith Hussin, a former McKinsey Partner, and Asim Husain, a former Google Engineering VP.