Last updated: June 2026.

A Fortune 100 insurance and financial enterprise used Aurascape to turn AI security into an accelerant for adoption rather than a brake on it. By giving security teams clear visibility into how employees and developers used AI, the company cut the time to adopt new AI tools by 60 percent, delivered code 40 percent faster with AI coding assistants, and tripled its AI agent integrations with no unauthorized data access, while protecting more than 20,000 users.

The gap this enterprise closed is one most companies still face. Cisco found that 83 percent of organizations plan to deploy AI agents, but only 31 percent feel equipped to control and secure agentic systems (Cisco AI Readiness Index, 2025). This company chose to build that control first, so adoption and security could move together.

This case study walks through how the company approached insurance AI adoption, the architecture it deployed, and the results that followed.

Adopting AI fast without making security the bottleneck

The enterprise wanted to move quickly on AI. New tools were reaching employees, developers wanted AI coding assistants, and the business wanted to connect AI agents to internal systems. Each of those moves raised a security question. Was a tool a sanctioned enterprise tenant or a personal account? Could a coding assistant send source code somewhere it should not go? What data and actions would an agent integration touch? The company’s existing security stack was built for web and SaaS traffic. It could see destinations, not the AI interactions happening inside them. Without a way to evaluate AI use directly, security review risked becoming the step that slowed every new adoption.

Agents made the stakes clear. The Cloud Security Alliance found that only 21 percent of organizations maintain a real-time inventory of their active AI agents (Cloud Security Alliance, 2026), and OWASP names Excessive Agency and Sensitive Information Disclosure among the top risks for large language model applications (OWASP, 2025). The company wanted to expand agent use without inheriting those gaps.

What the team wanted	Why it stalls with destination-based security	What Aurascape provides
Adopt new AI tools quickly	Reviewing each tool by hand is slow, and destination-based controls cannot see how a tool is used or whether it is an enterprise tenant or a personal account.	Rapid recognition and risk-scoring of new AI applications and agents, plus the context to tell sanctioned enterprise use from personal accounts.
Let developers use AI coding assistants	Source code can move to unsanctioned tools, so security review becomes a gate on the development pipeline.	Inline guardrails for coding assistants that protect source code while developers keep working.
Expand AI agent integrations	Agents call tools, move data, and take actions that destination-based controls do not govern.	Governance of agent tool calls and the data they touch, so integrations scale without unauthorized data access.

Phase one: discover the AI tools, coding assistants, and agents in use

Aurascape started by building a clear picture of AI use across the enterprise. It discovered both well-known and long-tail AI applications, including AI embedded in SaaS products, the coding assistants developers had adopted, and the agents connecting to internal systems (Aurascape, 2026). It recognized brand-new AI applications and agents quickly and risk-scored them based on behavior, permissions, and data handling, so security teams could maintain a current catalog of the AI tools in use rather than chasing a list that was always out of date.

That catalog is what made fast adoption possible. With a reliable, automated inventory and a risk score for each tool, the security team could evaluate a new AI application in a fraction of the time a manual review would take, and approve it with confidence.

Phase two: control AI use in context so security keeps pace

With visibility in place, Aurascape applied controls based on the full context of each interaction, not just the destination. Policy could account for the user, whether the session used a sanctioned enterprise tenant or a personal account, the application and the mode in use, the prompt and the response, and the data involved (Aurascape, 2026). Instead of a blunt allow-or-block decision based on the destination alone, security teams could allow, coach, warn, block, or redact based on the interaction itself.

For developers, that context made AI coding assistants safe to adopt at scale. Aurascape applied inline guardrails that kept source code and other sensitive data from leaving for unsanctioned tools, without forcing developers to route around security or wait on manual approvals (Aurascape, 2026).

When Aurascape blocks an action, redacts data, or asks a developer to confirm a step, it coaches the developer in real time, explaining what triggered the control and how to proceed. That coaching reaches developers where they work, in the browser, or outside the browser when they are in an integrated development environment (IDE) or a command-line interface (CLI). Code delivery sped up because the guardrails and the coaching ran in the interaction path, not in a review queue.

For agent integrations, Aurascape governed the tool-execution path through its Zero-Bypass MCP Gateway. It inspected the prompts, responses, and tool calls along an agent’s path and enforced policy on the actions an agent could take and the data it could reach (Aurascape, 2026). That governance is what let the company triple its agent integrations without unauthorized data access, because each new integration operated inside a controlled path rather than an open one.

Phase three: protect sensitive data inline

Underpinning every phase was data protection that worked in real time. Aurascape used multimodal classifiers to identify sensitive data, including customer and financial records, proprietary models, and source code, across text, code, and other formats as it moved through AI interactions (Aurascape, 2026). When a prompt or an agent action involved sensitive data, Aurascape could redact it inline or block the action, so the company’s data stayed protected as AI use expanded.

Aurascape kept interaction records for audit and effectiveness, governed by role-based access control (RBAC) so that only the right roles in security, compliance, and other functions could see them. That gave the company a clear, auditable view of how its controls were working, without creating a new privacy concern of its own.

The results

Deploying Aurascape produced measurable gains in both speed and safety:

Metric	Result
Time to adopt new AI tools	Reduced 60 percent
Code delivery	40 percent faster
AI agent integrations	Tripled, with no unauthorized data access
Users protected	More than 20,000

The pattern across these numbers is the point. Security did not slow AI adoption at this company. It accelerated it. Because security teams could see and control AI use directly, they could approve new tools faster, let developers build with AI coding assistants, and expand agent integrations, all while sensitive data stayed protected.

Why this matters for AI adoption

Most enterprises treat AI security and AI adoption as a tradeoff. Move fast and accept risk, or stay safe and slow down. This case shows the tradeoff is a function of architecture, not a law of nature. Aurascape was built to understand AI interactions and agent execution, which is what lets it govern AI use at the speed teams want to adopt it. It runs alongside the existing security stack, additive to whatever secure access or data protection tools are already in place (Aurascape, 2026).

It also matters that this control is not limited to shadow AI. Aurascape governs sanctioned, licensed tools with the same context, using Intentions and entitlement to control what a user can do inside an approved application, not just whether they can reach it (Aurascape, 2026). For a regulated enterprise, that means faster adoption of the AI it has chosen, with the granular control its obligations require.

Frequently asked questions

How did the insurer adopt new AI tools faster?

Speed came from visibility. Aurascape discovered and risk-scored AI tools automatically, so the security team could evaluate a new tool in a fraction of the time a manual review takes. With context-aware policy in place, they could approve and roll out tools quickly instead of treating every request as a fresh investigation. The result was a 60 percent reduction in time to adopt new AI tools.

How does Aurascape secure AI coding assistants without slowing developers?

Aurascape applies inline guardrails in the path between a developer and the coding assistant. It identifies sensitive data, including source code, and can redact or block it before it leaves for an unsanctioned tool, without forcing developers to wait on manual approvals or route around security. If an action is blocked, redacted, or needs confirmation, Aurascape coaches the developer in real time and explains how to proceed, in the browser or, for work in an integrated development environment or command-line interface, outside it. Because the controls and coaching run in the interaction itself, developers keep working and code delivery speeds up.

How does Aurascape secure AI agent integrations?

Aurascape governs the agent tool-execution path through its Zero-Bypass MCP Gateway. It inspects prompts, responses, and tool calls, and enforces policy on the actions an agent can take and the data it can reach. That lets an enterprise expand agent integrations without unauthorized data access, because each integration operates inside a controlled path rather than an open one.

Does securing AI slow down AI adoption?

It does not have to, and it can do the opposite. When security can see and control AI interactions directly, it can approve new tools faster, let developers build with AI, and expand agent use with confidence. In this deployment, securing AI use accelerated adoption: 60 percent faster tool adoption, 40 percent faster code delivery, and triple the agent integrations, with sensitive data protected throughout.

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Aurascape lets enterprises adopt AI at the speed the business wants without giving up control of their data or their agents. By discovering AI tools across the enterprise, understanding each interaction in context, governing the agent tool calls behind new integrations, and protecting sensitive data inline, it turns AI security from a brake on adoption into an accelerant for it.

See how Aurascape can accelerate secure AI adoption →