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You Are Already Accountable for an AI Footprint You Did Not Build.

Speak to an AI Security Specialist

AI has created a new attack surface that does not look like your old one.

It’s behavior: human, agent, and model behavior, that your existing controls were not built to see. When your next compliance review asks how you control data flowing to third-party AI services, “We have a policy” is not an answer. Your business should know what is going where, blocking what should not be shared, and have something tangible to show an auditor.

Globalgig helps your business understand its AI use, control risky interactions, and prove compliance across access, runtime, and posture.

Three Things Already Happening in Your Environment

01

An Employee Pasted Something They Should Not Have. You Just Don’t Know Who and What.

Your finance team dropped a contract into ChatGPT for a summary, HR shared employee data with an AI writing tool, or the ops team uploaded an ERP report to speed up analysis. None of it felt like a security issue.

But data that lived in a controlled environment is now with a third-party AI service. No alert was fired. You can see traffic went somewhere, but you cannot see what was shared.

Auditors want evidence of what has left your environment. Most organizations don’t have that record — and the data may have already been moved months ago.
02

Your AI Agents Have More Access Than Your Employees and Less Oversight.

When someone connects an automation tool or enables an AI-powered assistant, it has credentials, API tokens, and OAuth connections, often with permissions nobody explicitly reviewed.

Agents act very quickly. They do not second-guess instructions. If the access scope is wrong, the damage scales instantly. An agent does not need to be compromised to cause a problem, as misconfigured access, broad permissions, or unintended interactions are enough. . 

Nearly half of security leaders say they have already seen AI agents behave in ways that were unintended or unauthorized. A third have dealt with an actual incident or near-miss in the past 12 months.
03

Your Internal AI Systems Are Now Active Participants, Not Passive Tools.

When AI is deployed across internally developed applications, workflow automation, customer-facing tools, or operational systems, they become active participants inside the environment. They reason, generate actions, interact with other tools, and access enterprise data dynamically. This is what makes them useful, but it is also what makes them different from the software you have governed before.

The risk is not the model itself. It is what the model can access, trigger, retrieve, or influence across the wider stack. An AI application with too many permissions can expose data it should not touch, trigger unintended actions, or be manipulated through inputs in ways traditional security controls do not catch.

As these systems connect more deeply into business workflows and infrastructure, they create operational exposure inside the environment — not at the perimeter.

You Probably Have Some of These Tools, but This Is Not the Problem

Your endpoint tool can see that a user accessed an AI tool. It cannot see what they shared with it. Your DLP software watches for file movement, not prompt content. Your identity governance was designed for human users, not machine identities that arrive pre-credentialed and expand permissions over time.

Your business is dealing with a new kind of actor that does not behave like a user, look like a service account, or stay still long enough for legacy controls to keep up.

The risk is not that AI is being used. It is that it is already being used in ways you cannot fully account for.

The Control Layer AI Security Requires

AI exposure does not sit in one place. It moves through browsers, endpoints, identity systems, APIs, cloud workloads, and AI services that were never designed to operate together securely.

This is why AI security is not one product decision. It is an architecture, visibility, and enforcement problem that spans the environment.

See How Globalgig Brings It Together

From AI Access to Runtime Protection

Not a Reseller. Not an MSSP. Not a Consultancy.

Globalgig designs, integrates, and operates the control layer for enterprise AI security.
We bring the required network, identity, endpoint, browser, runtime, governance, and response layers into one managed architecture, using Palo Alto Networks technologies and your existing security ecosystem.

The Control Layers We Bring Together

Network controls: How users, devices, branches, cloud environments, and AI traffic move through enforceable control points.
Identity enforcement: Who or what can access AI applications, from where, under what conditions, and with which permissions.
Endpoint and browser policy: The user, device, browser, and session controls that shape how AI tools are accessed and used.
AI runtime protection: How agents, models, APIs, workloads, and machine identities are monitored and controlled while they execute.
AI governance: How AI usage policies, approved tools, data access, and audit evidence are defined, enforced, and maintained.
Continuous response: How risky usage, policy drift, alerts, and incidents are reviewed, escalated, and resolved as the environment changes.

One Operating Model. One Commercial Relationship


We bring licensing, integration, policy design, deployment, and managed operations into a single engagement, so your team is not left coordinating separate vendors, contracts, and support paths.


You keep visibility and ownership of your environment. We make the security model work in practice.

AI security doesn’t start with AI. It starts with whether your traffic flows through a control point, whether identity is consistently applied, and whether your environment can enforce policy in real time.

We’ve seen what happens when teams start in the wrong place. It usually ends in more tools, more complexity, and the same gaps.”

Jamie Pugh , Globalgig CTO

Built for Existing Environments. Not Idealized Ones.

We do not start with a reference architecture and ask your environment to fit it.

Most AI security implementations fail in the gap between design and reality. The legacy firewall that predates the SD-WAN migration. The identity provider that was never fully integrated. The AI tools your employees are using that nobody officially approved.

We design around operational reality because we stay responsible for how the environment performs after deployment.

Stage 1

Discovery

We start by understanding your network topology, identity layer, and how access is provisioned, your existing security controls, and where they are actually enforced. Globalgig looks at your compliance requirements, the evidence your business must provide, your team’s capacity, and what they can realistically own.

Then we map the AI layer on top of that, including the tools in use — both sanctioned and unsanctioned. What agents and integrations exist, and what they can access. We also look at what data flows through them, whether any of it is monitored, and where AI traffic moves outside a control point.

Most organizations discover two things at this stage: their AI footprint is larger than they thought, while the gaps are not only in the AI tools — they are in the underlying stack they sit on.

Stage 2

Gap Analysis

We map your existing controls against your AI-specific risk. Where is AI traffic flowing outside a control point? Globalgig explores where machine identities exist without governance and whether internally deployed AI has security access that was never reviewed.

Stage 3

Architecture Design

We design the control layer for your specific environment. Access security for public AI tools. Runtime protection for internally deployed AI. We look at posture management across your AI footprint, integration with your existing stack, and create decommissioning plans for what should not stay.

Stage 4

Deployment

Controls are validated in your environment before traffic shifts. Integration happens in phases, with rollback strategies built in at each stage. Your team retains full visibility throughout, while we handle deployment.

Nothing goes live until it has been validated against how your environment actually behaves, not how it looked in the design.

Stage 5

Continuous Operations and Proactive Security Improvement 

After deployment, our engagement does not end — it changes shape. Our Security Operations Center (SOC) continuously monitors your environment. When a new AI tool appears, we bring it under policy. When an agent’s behavior shifts, we detect it, and if control drifts, we fine-tune it. 

When something goes wrong, the team responding already knows your architecture, policies, and what has changed. That context is what makes the difference between containment and a three-hour reconstruction across disconnected tools. 

How far we go from there depends on your preferred service tier, ranging from expert analysis and recommendations for your employees to full operational ownership, where we identify, respond to, and implement solutions.

Learn About Security Management

When something goes wrong, the team responding already knows your environment.

They know how the controls were designed, why policies were configured that way, and what has changed. This is the difference between containment in minutes and spending hours reconstructing what happened.

Resources

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Be Ready to Validate Your Company’s AI Use

If AI risk is already on your board’s agenda, but you are not confident your environment can back it up, we will help you sense-check it.

Because when this gets raised internally, the question is not whether AI tools are being used. It is whether you can prove what data has been shared with AI tools, who shared it, and under what conditions.