Srikrishna Shankavaram, Head of AI Security, Zebra Technologies, outlines the delicate balancing act between securing AI agents and governing them as digital identities in this exclusive feature.
2026 is touted as the year AI moves from speculation and interest to real-world deployment and value. Yet 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.
It suggests more and better advice, and an ecosystem of support are needed to make agentic projects a success, which must include addressing new security considerations.
There are early signs that the rise of agentic AI is transforming how organisations operate. In retail, AI agents are already managing inventory, optimising pricing, assisting customers, forecasting demand, and automating supply chain decisions.
At the same time, logistics organisations are deploying autonomous agents to coordinate warehouse operations, route shipments, manage fleet utilisation, track inventory movement, and resolve operational exceptions in real time.
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.
New security considerations in 2026
While the business benefits are significant, the security implications are equally profound. As AI agents gain access to enterprise applications, APIs, devices, customer information, and operational systems, organisations must address a critical question: How do we secure AI agents that increasingly operate like employees but at machine speed and scale?
Traditional AI security concerns such as prompt injection, data leakage, and model vulnerabilities remain important. 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. So, what are the new security considerations and methods to address them in 2026?
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 MCP and tool access
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.
For retailers, this could mean restricting access to pricing engines, loyalty systems, or inventory platforms. For logistics providers, it may involve controlling access to transportation management systems, warehouse automation platforms, carrier networks, and shipment tracking services. A compromised tool connection can provide attackers with a direct path into critical operational workflows.
Secure runtime operations
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, behavioral monitoring, approval checkpoints for high-risk actions, anomaly detection, and emergency kill switches. These controls help identify and contain risky behavior 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 behavior due to changing inputs, evolving objectives, or malicious manipulation.
Don’t forget the devices
Agentic AI increasingly interacts with physical infrastructure. In retail environments, agents may access point-of-sale systems, kiosks, handheld scanners, smart shelves, and IoT devices. In logistics operations, agents often interact with warehouse scanners, robotics systems, automated storage equipment, vehicle telematics, fleet management platforms, and industrial IoT sensors.
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.
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.
Continuously red team your agents
Traditional penetration testing is no longer sufficient. 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.
Retail and logistics 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.
As AI agents gain access to inventory systems, pricing engines, warehouse platforms, transportation networks, customer data, and operational devices, the key question will be getting the balance right between securing AI agents like software and governing them as digital identities.
Read more about AI agents for retail and logistics here.
Image Credit: Zebra Technologies
Source: Tahawul Tech