Artificial intelligence is rapidly moving from experimentation into business operations. As organizations deploy AI agents across workflows, applications, and data environments, a new challenge is emerging: how can enterprises scale AI while maintaining security, governance, visibility, and control?
A recent development in this space is the $7.5 million seed funding raised by AI enablement company Xpander. The funding round was led by Pico Venture Partners, with participation from Emerge Ventures, Samsung Next, and Seedil. Xpander plans to use the investment to accelerate its market expansion.
Founded in 2024 by former AWS engineers, Xpander is developing infrastructure designed to help organizations adopt, build, run, secure, and manage AI agents across their environments.
Why AI Agent Management Is Becoming Critical
Traditional software applications generally operate according to predefined workflows and permissions.
AI agents introduce a different operating model.
An AI agent may interpret information, interact with applications, access data, use tools, and perform actions based on a task or objective. When organizations deploy large numbers of agents, managing those activities can become increasingly complex.
Enterprises therefore need visibility into questions such as:
• Which AI agents are operating within the organization?
• What systems and data can each agent access?
• Which employees or applications can create or modify agents?
• What actions are agents permitted to perform?
• How are agent activities monitored?
• What happens when an agent behaves unexpectedly?
• How can organizations demonstrate compliance with AI governance requirements?
Without appropriate controls, rapid AI adoption can create a significant governance gap.
The Importance of Portable AI Infrastructure
According to the report, Xpander’s platform uses a vendor-neutral universal agent harness that allows AI agents to operate as portable workloads and securely render interfaces when required. The approach is intended to provide organizations with greater control over how agents are built, deployed, and managed across products, workflows, and data.
Vendor neutrality can become particularly important as enterprises adopt multiple AI models and platforms.
Organizations may use different models for customer service, software development, analytics, cybersecurity, document processing, internal automation, and other business functions.
A centralized governance approach can help security teams establish consistent controls across these environments.
Multi-Agent Workflows Create New Security Questions
AI agents are increasingly being designed to collaborate.
Xpander’s platform includes functionality intended to support agent teammates and collaborative multi-agent workflows.
This creates significant opportunities for automation, but it also introduces additional security considerations.
When multiple agents interact, organizations should evaluate:
• Agent-to-agent permissions
• Data access boundaries
• Authentication mechanisms
• Tool and API permissions
• Communication channels
• Privilege escalation risks
• Prompt injection risks
• Unauthorized actions
• Sensitive data exposure
• Agent behavior monitoring
An AI agent should not automatically receive the same level of access as a human administrator simply because it is capable of performing complex tasks.
AI Governance Must Include Security
AI governance is sometimes viewed primarily as a compliance or policy function.
In practice, effective AI governance needs to connect business requirements with cybersecurity controls.
Organizations should establish clear policies covering:
AI Asset Inventory
Organizations need visibility into the AI models, applications, agents, APIs, datasets, and third party AI services being used across the enterprise.
Identity and Access Management
Every AI agent should have clearly defined identities, permissions, and access boundaries.
Least Privilege
AI agents should receive only the permissions required to complete their assigned tasks.
Continuous Monitoring
Agent behavior should be monitored for unusual activity, unauthorized access, excessive data retrieval, and unexpected actions.
Human Oversight
High-risk decisions and sensitive operations should include appropriate human review and approval.
Security Testing
AI systems should undergo security assessments before and after deployment, particularly when they interact with enterprise systems or sensitive information.
Data Governance
Organizations need controls around what information AI systems can access, process, retain, and transmit.
Compliance Is Not Enough Without Operational Controls
AI regulations and cybersecurity frameworks are becoming increasingly important as enterprises adopt AI at scale.
However, documentation alone does not secure an AI environment.
Organizations need to translate governance requirements into technical controls.
For example:
A policy may state that sensitive customer information must not be exposed to an AI system.
A mature security program should then implement controls that identify sensitive information, restrict access, monitor AI interactions, and alert security teams when policy violations occur.
This is where AI governance and cybersecurity need to work together.
Industries That Need Strong AI Governance
The rapid adoption of AI agents affects almost every major industry.
Financial Services
Banks, financial institutions, insurance companies, and FinTech organizations can use AI agents for customer service, fraud detection, financial analysis, software development, and operational automation.
Because these environments handle highly sensitive financial and personal information, AI access controls, monitoring, data governance, and compliance are critical.
Healthcare
Healthcare organizations are increasingly exploring AI for clinical support, administration, research, documentation, and patient services.
Strong AI governance can help protect sensitive healthcare information and support regulatory requirements.
Retail and E-Commerce
Retail organizations can deploy AI for customer support, personalization, supply chain operations, inventory management, and fraud detection.
Security controls can help prevent unauthorized access to customer and business data.
Manufacturing
Manufacturers are increasingly combining AI with industrial systems, IoT environments, supply chains, and business applications.
AI governance needs to account for both enterprise information and operational technology risks.
Government
Government agencies manage sensitive citizen information and critical services.
Strong identity controls, monitoring, data governance, and AI security assessments can help reduce risks associated with autonomous systems.
Technology and SaaS
Technology companies are among the fastest adopters of AI agents.
Security must be incorporated into AI development, deployment, APIs, cloud infrastructure, and software development workflows.
Building a Secure AI Operating Model
Organizations preparing for widespread AI agent adoption should consider developing an AI security framework that includes:
• AI asset discovery and inventory
• AI risk assessments
• Agent identity management
• Role-based access controls
• Least privilege enforcement
• Secure API architecture
• Data classification and governance
• Prompt injection testing
• Model security assessments
• Agent behavior monitoring
• Human approval workflows
• Continuous security validation
• Third party AI risk management
• Incident response procedures
• AI security awareness training
These controls can help organizations move from uncontrolled AI experimentation toward a more structured and secure AI operating environment.
The Bigger Picture
The funding raised by Xpander reflects a broader market trend.
Enterprises are not simply looking for AI models. They increasingly need infrastructure that allows them to manage AI systems securely at scale.
As AI agents become integrated into business processes, the security question will shift from whether organizations should use AI to how they can safely manage autonomous systems operating across enterprise environments.
AI governance will therefore become an important component of enterprise cybersecurity.
Conclusion
The growth of AI agents represents a major opportunity for organizations to automate processes, improve productivity, and create new digital services.
But scaling AI without governance can introduce new risks involving identity, data access, permissions, application security, and autonomous decision-making.
The next stage of enterprise AI adoption will require more than powerful models. Organizations will need visibility, governance, security testing, monitoring, access controls, and human oversight.
Companies that build these safeguards into their AI strategy from the beginning will be better positioned to adopt AI responsibly while protecting sensitive information and meeting evolving compliance expectations.
About COE Security
COE Security partners with organizations in financial services, healthcare, retail, manufacturing, and government to secure AI-powered systems and ensure compliance.
Our offerings include:
• AI-enhanced threat detection and real-time monitoring
• Data governance aligned with GDPR, HIPAA, and PCI DSS
• Secure model validation to guard against adversarial attacks
• Customized training to embed AI security best practices
• Penetration Testing (Mobile, Web, AI, Product, IoT, Network & Cloud)
• Secure Software Development Consulting (SSDLC)
• Customized CyberSecurity Services
In addition, COE Security helps organizations establish secure AI governance programs through AI risk assessments, AI security testing, agent security assessments, identity and access management reviews, API security testing, cloud security assessments, data governance, vulnerability management, secure software development, DevSecOps consulting, third party AI risk assessments, continuous monitoring, and compliance readiness.
For financial services and FinTech organizations, we help secure AI systems handling financial and customer information while supporting governance and compliance requirements.
For healthcare organizations, we help protect sensitive data and assess AI applications, cloud environments, APIs, and supporting infrastructure.
For retail and e-commerce organizations, we help secure customer-facing AI applications, data platforms, APIs, and third party integrations.
For manufacturing organizations, we help assess AI, IoT, cloud, application, network, and operational technology security risks.
For government organizations, we help strengthen AI governance, data protection, identity security, monitoring, application security, and compliance programs.
For technology and SaaS companies, we help integrate security into AI development, agentic workflows, APIs, cloud environments, and software development processes.
Our goal is to help organizations adopt AI securely, identify vulnerabilities before attackers can exploit them, protect sensitive information, and build resilient and compliant digital environments.
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