How companies can deploy AI agents safely

Companies looking to leverage autonomous artificial intelligence (AI) agents for enhanced productivity must first establish robust data, cybersecurity, and governance frameworks, experts advise. These AI agents, capable of automating complex workflows and assisting with daily tasks, promise significant gains but also pose substantial risks if not properly managed.

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Every AI agent is a digital worker with tools, access and authority. Its safety depends on the foundations beneath it: trusted data, strong controls and a mature operating model. When those foundations are sound, agents multiply capability. When they are weak, agents multiply risk faster than people can react.

Edward Chen, chief AI officer at NCS, emphasizes that each AI agent functions as a digital worker with inherent tools, access, and authority. "Its safety depends on the foundations beneath it: trusted data, strong controls and a mature operating model," Chen stated. "When those foundations are sound, agents multiply capability. When they are weak, agents multiply risk faster than people can react."

Organizations are cautioned against treating AI agents as mere software modules. Instead, they should be onboarded with the same rigor applied to human employees, involving clear roles, defined access limits, consistent supervision, and established accountability. "Safe AI is not a technical specification you bolt on," Chen explained. "It is the result of disciplined design – knowing what the agent can access, what it can act on, when it must escalate and how every action can be traced back to human accountability."

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ORGANISATIONS SHOULD NOT GOVERN AGENTS LIKE SOFTWARE MODULES. THEY SHOULD ONBOARD THEM LIKE DIGITAL WORKERS – WITH CLEAR ROLES, ACCESS LIMITS, SUPERVISION AND ACCOUNTABILITY.

Failures in AI adoption often stem from a rush to implement the technology without adequate foundational support. Fragmented data can lead to AI inconsistencies, while inadequate cybersecurity controls can create new vulnerabilities. Furthermore, operating models designed for predictable, deterministic systems may struggle with the less predictable nature of AI, blurring lines of accountability. Chen highlighted that the shift from simple automation to AI agents requires a deeper understanding, as agents can interpret tasks, choose paths, and act across multiple systems, necessitating pre-deployment clarity on roles, access, supervision, and accountability.

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Safe AI is not a technical specification you bolt on. It is the result of disciplined design – knowing what the agent can access, what it can act on, when it must escalate and how every action can be traced back to human accountability.