Globalgig Managed AI Security: What Enterprises Need to Know

The News

Globalgig, a San Antonio-based managed security and network services provider, announced an expansion of its portfolio to cover enterprise AI security. The new offering bundles four distinct service areas: AI Security Posture Management, AI Access Security, AI Runtime Security, and AI Security Operations. The company cites a 2026 Cost of a Data Breach Report finding that shadow AI was involved in 43% of breaches this year, and frames the announcement as a response to enterprises adopting AI faster than they can secure it.

Analyst Take

The Shadow AI Problem Is Bigger Than Most Security Budgets

The headline statistic Globalgig leads with is striking: shadow AI implicated in 43% of breaches, more than double the prior year. That’s a threat surface expanding faster than the security market has responded. The product logic follows directly. Enterprises that deployed AI tools throughout 2024 and 2025, often team by team with minimal central oversight, now have sprawling exposure across models, agents, and data pipelines that their existing security stacks weren’t designed to see. Globalgig’s bet is that managed services, not point products, are how most organizations will close that gap.

That bet looks well-timed. The complexity of securing AI is genuinely different from securing traditional software. You’re not just scanning code and patching CVEs. You’re dealing with prompt injection, tool misuse by autonomous agents, model tampering, and sensitive data leaking through generative interfaces. Each of those is a distinct attack surface. Globalgig’s four-part framework (posture, access, runtime, operations) maps directly to that complexity, and wrapping it in a managed model means customers don’t have to build or staff the capability themselves.

Developer Velocity Creates the Exposure That Runtime Security Has to Catch

There’s a deeper structural issue this announcement speaks to. AI adoption in enterprise software development has accelerated dramatically, and the governance hasn’t kept pace. ECI Research’s Google GovTech Survey found that 49.6% of respondents expect AI to assist or generate between 26% and 50% of their organization’s code within the next 12 months. When nearly half of your code output is AI-assisted, the security and provenance questions around that code multiply fast. Globalgig’s AI Runtime Security layer, which covers scanning of model and agent code before deployment and real-time defense during execution, targets the kind of risk that emerges when development moves faster than security review cycles.

The access security component deserves equal attention. Employees using generative AI tools are, often unknowingly, creating data loss vectors through the prompts and uploads they send to external models. ECI Research’s survey also found that 31.8% of respondents identify FedRAMP and compliance approval friction for AI vendors as the single largest blocker to widespread AI adoption in developer workflows. That number reveals a tension: security requirements are slowing adoption, but where adoption has outpaced security review, breach exposure is rising sharply. Globalgig’s managed model threads that needle by providing the compliance-grade oversight that makes AI use defensible without requiring customers to halt deployment while they build the controls themselves.

Why the Managed Model Is the Competitive Differentiator

The technology stack here is Palo Alto Networks, which is a credible foundation. But Globalgig’s CTO makes a deliberate point of saying the relationship is about platform completeness and pace of advancement, not product loyalty. That framing matters. In the managed security market, the technology underneath is increasingly commoditized. What’s being sold is the operational model: 24/7 SOC coverage, integrated network context through Orchestra Insight, and a flexible ownership model where customers can shift how much of the AI layer they run themselves versus hand to Globalgig. For enterprise security buyers who are already managing tool sprawl, that flexibility is genuinely attractive.

For developers and security architects, the architecture question worth examining is how Orchestra Insight’s network-layer visibility integrates with the AI security controls. Real-time traffic visibility underneath AI workloads is a meaningful capability because many AI-specific threats, including data exfiltration through model outputs and agent-to-agent communication abuse, manifest at the network layer before they surface in application logs. If that integration is as tight as the announcement implies, it represents a detection advantage over point solutions that only see the application layer.

Looking Ahead

The managed AI security market is going to consolidate quickly. The vendors who will win are those who can demonstrate measurable outcomes, not just coverage breadth, and Globalgig’s CEO is already framing the pitch around owning the outcome rather than delivering a product. Expect competitors to respond with similar bundles over the next two to three quarters, which means Globalgig’s window to establish differentiation through customer references and breach-prevention metrics is short. The company’s claim to be one of the fastest-growing MSSPs this year suggests the commercial momentum is there; the execution question is whether the delivery model scales without degrading the managed service quality that drives that growth.

For enterprise security buyers, the near-term evaluation question is straightforward: do you have complete inventory of every AI system, model, and agent running in your environment? Most don’t. ECI Research’s survey data shows that governance of AI agents is still largely confined to approved use cases under defined policies, with significant portions of organizations still operating in pilot or experimental modes. As that experimental footprint scales into production, the attack surface Globalgig is targeting will only grow. Organizations that delay building or buying AI security controls now are accepting a risk posture that will be substantially harder and more expensive to remediate after a breach than before one.

Authors

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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  • With over 15 years of hands-on experience in operations roles across legal, financial, and technology sectors, Sam Weston brings deep expertise in the systems that power modern enterprises such as ERP, CRM, HCM, CX, and beyond. Her career has spanned the full spectrum of enterprise applications, from optimizing business processes and managing platforms to leading digital transformation initiatives.

    Sam has transitioned her expertise into the analyst arena, focusing on enterprise applications and the evolving role they play in business productivity and transformation. She provides independent insights that bridge technology capabilities with business outcomes, helping organizations and vendors alike navigate a changing enterprise software landscape.

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