Infor: Crossing the Data Security Barrier in Scaling AI

While business confidence in AI continues to strengthen, the reality for many enterprises is that deployment at scale brings significant cybersecurity and data governance challenges.

New research from Infor reveals that more than half of businesses are struggling to scale AI securely, with data protection concerns emerging as the primary barrier to implementation.

Aimed at addressing this critical gap between ambition and secure execution, industry cloud provider Infor has introduced new capabilities across its Velocity Suite and Infor Agentic Orchestrator, delivering industry-specific solutions built on secure, compliant architecture.

"At Infor, agentic AI isn't a feature we bolted on. It's the culmination of two decades of deliberate foundation building," Kevin Samuelson says, CEO of Infor.

"Our industry-specific platforms, multi-tenant architecture and deep process intelligence give our agents a level of contextual precision that generic AI simply cannot replicate.

"A purchasing agent at a healthcare provider and one at a discrete manufacturer aren't the same agent, they shouldn't be."

This industry-specific approach could mean that security protocols and compliance frameworks are embedded at the architectural level, rather than applied as an afterthought.

Data sovereignty emerges as primary concern

The study, conducted across the US, UK, France and Germany, shows that making the technology work at scale while maintaining security posture is a major operational challenge.

With 70% of businesses in the US and 74% in the UK report that they have the capability to manage AI implementation, yet this readiness does not translate into secure execution, with structural barriers blocking effective deployment.

Data security concerns represent the most prominent barrier to AI implementation, according to the Infor research.

Around a third (34%) of businesses in the US and Germany, 32% in France and 45% in the UK all point to variants of data sovereignty, security and privacy concerns as factors that prevented them from advancing their AI strategies.

Legacy systems present particular vulnerabilities, whilst inconsistent governance frameworks and fragmented data across multiple systems create attack surfaces that could compromise AI effectiveness.

These execution gaps appear in distinct ways across different industries.

Legacy infrastructure plagues manufacturing the most, governance and compliance requirements hinder healthcare while distributors face fragmented data across various supply chains.

Embedding generic AI into these workflows without accounting for their underlying security architecture produces outcomes that cannot scale safely.

Closing this execution gap requires not only deep domain-specific industry knowledge but also robust security frameworks that address sector-specific compliance requirements.

"That specificity is what allows us to clearly articulate the ROI and deliver on it," Kevin says. "We're not selling automation for its own sake. We're selling measurable outcomes for the industries by meeting our customers where they are with AI and providing a clear, simple and efficient path to where they want to be."

Securing AI with trusted architecture

As AI models are only as good as the data they are trained on, fragmented data across multiple systems could result in models that struggle to deliver outcomes whilst maintaining data integrity.

With only 25% of businesses saying their data is mature enough to support reliable AI, this gap could not be more crucial from a security perspective.

Secure, responsible and compliant deployment of AI agents is a necessity in the current threat landscape.

Beyond data security concerns, the lack of internal AI talent (25%), unclear ROI (23%) and high cost of AI (23%) are additional barriers to implementation.

The ability for AI to perform tasks autonomously was ranked by 32% as a factor for long-term AI success.

The security implications here are paramount. Within the Infor Industry Cloud Platform, Infor's Agentic Orchestrator facilitates a "trusted, transparent infrastructure layer that enables Industry AI Agents to move from isolated tasks to coordinated workflows".

Building visibility and control frameworks

The updated capabilities released by the company operate across three critical areas for security teams: orchestration, interoperability and observability.

The platform offers multi-agent operation with supervisor agents and specialised task agents. The supervisor agents are pre-trained to flag anomalies which the human in the loop can then act upon, providing a critical security checkpoint.

Infor Agentic Operator addresses AI integration security concerns through standardised Model Context Protocol (MCP), offering secure actions and controlled access to data. This architectural approach could mean that security policies are enforced consistently across all AI operations.

Observability is critical in agentic use cases, particularly from a security and compliance perspective. Infor's latest visibility features offer three particular capabilities: inline thoughts, evaluation frameworks and focus mode.

These allow complete user control and oversight, ensuring that AI operations remain auditable and compliant with regulatory requirements.

Speaking to the success of Infor's approach to agentic implementation, Mickey North Rizza, Group Vice-President of Enterprise Software for IDC notes: "It is very clear that Infor's clients are finding sustained economic value with their path to the agentic enterprise and they love the journey with Infor."