Zebra Technologies Corporation, a global leader in digitising and automating workflows to deliver intelligent operations, recently highlighted the urgent security risks and measures needed to implement and maintain AI agents in business for sectors like retail and logistics.
2026 has witnessed several high-profile cases of AI agents used in increasingly autonomous cyber-attacks and agents breaking out from secure environments. While one global analyst firm predicts 40% of agentic AI projects will be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls.
“These systems are no longer simple chatbots or assistants—they are becoming autonomous digital operators capable of making decisions and executing actions across business-critical systems”, said Srikrishna Shankavaram, Principal Engineer, AI Security, Zebra Technologies. “More and better advice and an ecosystem of support are needed to make agentic projects a success, which must include addressing new security considerations”.
As AI agents gain access to enterprise applications, APIs, devices, customer information, and operational systems, organisations need address a critical question: How do we secure AI agents that increasingly operate like employees but at machine speed and scale? Shankavaram sets out the key risks and measures IT and security teams need to implement as part of successful AI agent transformation.
Treat AI agents as digital identities
Every AI agent should be managed as a non-human identity rather than merely an application. Just as organisations govern employee access through identity and access management systems, AI agents require unique credentials, role-based permissions, lifecycle management, and continuous auditing.
In retail, an inventory optimisation agent should not automatically gain access to customer payment systems. In logistics, a route-planning agent should not be able to modify warehouse management configurations without authorisation. Applying least-privilege principles ensures that agents can perform their intended tasks while minimising security exposure. Without proper governance, an unmanaged AI agent can quickly become a highly privileged insider capable of accessing sensitive systems and data.
Govern and secure MCP, tool access, runtime operations
The Model Context Protocol (MCP) is emerging as a key mechanism for connecting AI agents with enterprise tools, databases, applications, and external services. While this connectivity enables powerful automation, it also expands the attack surface. Organisations should establish strong governance around MCP servers and tool integrations. This includes validating trusted MCP endpoints, implementing allowlists for approved tools, inspecting contextual information passed to models, and continuously monitoring tool usage. A compromised tool connection can provide attackers with a direct path into critical operational workflows.
Security cannot stop once an AI agent is deployed. Autonomous systems must be monitored continuously throughout their operational lifecycle. Runtime security controls should include policy enforcement, behavioural monitoring, approval checkpoints for high-risk actions, anomaly detection, and emergency kill switches. These controls help identify and contain risky behaviour before it impacts business operations.
For example, a retail agent that suddenly attempts to modify thousands of product prices should trigger investigation. Similarly, a logistics agent that begins rerouting large volumes of shipments or altering delivery schedules outside established policies should be flagged immediately. Continuous runtime visibility is essential because even well-trained agents can drift from expected behaviour due to changing inputs, evolving objectives, or malicious manipulation.
Build security across every layer
Organisations should adopt a layered security approach spanning agent identities, MCP and tool governance, runtime protection, endpoint trust, API security, and data protection. Strong authentication, least-privilege access, token management, behavioural analytics, and continuous monitoring should work together to provide defence in depth.
A compromised endpoint can influence the decisions made by an AI agent, creating downstream business risks. Device trust, posture validation, endpoint security, and continuous compliance monitoring should therefore be integrated into any Agentic AI security strategy.
Agentic AI systems must be continuously evaluated against threats such as prompt injection, goal hijacking, privilege escalation, tool abuse, workflow manipulation, and business logic attacks. Organisations should regularly test complete agent workflows rather than individual components. The objective is to understand how autonomous systems behave under real-world adversarial conditions and identify weaknesses before attackers do.
Getting the balance right
Retail and logistics organisations are rapidly deploying AI agents to automate increasingly critical business processes. The leaders in this new era are not those who deploy the most intelligent agents, but those that also establish the strongest foundations of trust, governance, and security.
“Traditional AI security concerns such as prompt injection, data leakage, and model vulnerabilities remain important”, said Shankavaram. “However, security leaders are now confronting a broader challenge – governing autonomous agents that can access tools, execute workflows, interact with physical devices, and influence business outcomes without constant human oversight. The conversation is shifting from securing AI models to securing AI-powered digital workforces”.
Image Credit: Zebra Technologies
Source: Tahawul Tech