Enable safe AI adoption through secure paved paths
Remove the constraints that inhibit AI enablement
A guest article by Jeremy Snyder is the founder and CEO of FireTail, an end-to-end AI security platform that provides the visibility, insight, and control necessary to enable secure AI adoption.
Let’s be real. Your developers, your marketers, your sales team—everyone is already using AI. The real question isn’t if they’re using it, but if you have any visibility into how they’re using it. We’re talking about engineers pip install-ing the latest open-source model and making direct API calls from a forgotten Lambda function, completely bypassing security review.
This isn’t a future problem. One report found AI usage inside companies grew 485% last year, and a staggering 90% of that happened in unsanctioned accounts. The knee-jerk reaction from security is to lock it all down with a shiny new “AI Firewall.” It feels like the right move, but it’s a trap. And it’s making your security posture worse, not better.
Top-down AI control fails engineering teams
The pressure to adopt AI is immense because while you’re debating it, your competitor is using it to gain an advantage. This pressure often leads to a rush for control, which inevitably backfires, creating what we call “Shadow AI”.
Let’s use a tangible example that every engineering leader can appreciate. Your security team, with the best intentions, implements a policy to block source code from being sent to external LLMs. On paper, this is a perfect control. In reality, it breaks essential coding assistants like GitHub Copilot for your entire engineering department, grinding productivity to a halt.
What happens next? Your highly-paid, motivated developers do what any good engineer does when faced with a roadblock: they find a workaround. They’ll tether to their phone to bypass the corporate network or spin up a personal Codespaces environment where your proxy has no visibility. Your attempt to control the risk has failed. Worse, you’ve made the risk completely invisible.
Build a paved path that makes secure AI the default
This isn’t our first rodeo. We saw this pattern with every major technology wave. Security teams tried to block PDAs, then SaaS applications, then the public cloud. Every single time, the business moved faster, and the department of “No” was eventually forced to become the department of “Yes, if...”.
AI is just the next, much faster wave. Instead of repeating history, we need to shift our mindset from being a gatekeeper to being an enabler. Security’s role should be to build a “Paved Path”—a secure, efficient, and easy-to-use pathway for AI development that makes the secure route the path of least resistance.
This approach starts with a core principle: Visibility Before Control. You simply can’t govern what you can’t see. Effective AI governance begins with a complete inventory of every AI model, service, prompt, and API being used across your entire organization, from your workforce to your workloads. And crucially, this discovery must happen without a restrictive inline proxy that introduces latency and creates a single point of failure.
The anatomy of a secure AI platform built on this principle includes:
Discovery and Inventory: Tools that automatically scan your code, cloud accounts, and user activity to see what AI is actually being used.
Risk-Informed Security Posture: The ability to understand the inherent risks of each workload, prompt, and LLM your teams are using.
Observability: Logging and monitoring all interactions with AI services to understand data flows and identify risky behavior in real-time.
Informed, Granular Policies: Creating context-aware rules instead of one-size-fits-all policies. For example, you can allow the engineering team to use Copilot while blocking the finance team from uploading sensitive documents to public LLMs.
Automated Guardrails: Providing real-time alerts and controls for non-compliant or risky behavior without breaking developer workflows.
Measure success by developer adoption and velocity
Getting started is more straightforward than you think. Here are some practical first steps for engineering leaders:
Start with Visibility: The first step is always to get a comprehensive inventory of all AI usage across the development lifecycle.
Identify High-Risk Designs: Use static analysis (SAST) to find where your application is concatenating user input directly into prompts without sanitization, opening you up to prompt injection. Or find agentic AI workflows that lack proper sandboxing or rate limiting.
Identify High-Risk Patterns: Use your observability data to find token budget overruns that might indicate a model DoS attack, or spot an engineer accidentally pasting a production database schema into ChatGPT for debugging.
Collaborate on “Starter” Policies: Work directly with a small, trusted team of developers to build your initial policies. The goal is to create a secure-by-default ‘golden path’ that developers want to use because it comes with pre-configured logging, security, and access to the best models.
You’ll know your Paved Path is working when you track the metrics that truly matter:
Developer Adoption: Are your developers choosing to use the sanctioned, secure platform? A high adoption rate is the ultimate sign of success.
Velocity: Does this approach accelerate the secure deployment of new AI features? Track the time from idea to production for AI-powered projects to prove it.
Make security the engine of AI innovation
By taking a “Paved Path” approach, engineering leaders can transform security from a source of friction into a genuine strategic advantage. This model allows you to say “yes” to AI, providing your teams with the tools they need to innovate quickly while giving the business confidence that it’s being done securely.
Ultimately, this is about treating AI security as an engineering problem, not a policy problem. It’s about building a scalable, resilient platform that enables innovation, instead of a brittle gate that will eventually be knocked down.
Our Take: What Happens Next?
Jeremy and the FireTail team nailed the first hurdle: secure AI enablement is non-negotiable. The “Paved Path” ensures your developers are using AI safely and quickly.
But this speed introduces the next major challenge: faster coding exposes every hidden weakness in your delivery pipeline. Once PRs open faster, where does the bottleneck shift? To code review, CI/CD capacity, or unpredictable release cadences?
You can’t manage what you don’t measure. High adoption rarely means faster delivery if you can’t close the measurement gap between AI usage and actual business outcomes.
LinearB is the platform built for this stage. We combine AI usage data with workflow automation and delivery metrics, helping engineering leaders see where AI creates true leverage, and where it simply shifts constraints downstream.
→ See how LinearB helps you measure the true impact of AI velocity on your delivery outcomes.