Trustworthy AI Starts with Clear Roles under ETSI EN 304 223
The ETSI EN 304 223 standard puts AI security on the same footing as any critical IT system. Its starting point is clear: organizations need a set of minimum, verifiable requirements that accompany models from conception to retirement, with controls that address risks unique to AI rather than only traditional threats. The standard lays out secure design principles, governance controls, and technical safeguards across the entire lifecycle.
The document stresses that attacks on AI systems take specific forms. It is not only about infrastructure flaws, but also about manipulating the learning process and the context in which a model operates. Hence it includes mitigations for data poisoning, prompt and instruction injection, extraction or inference of sensitive information from model outputs, and supply chain vectors. Addressing these issues requires controls over data provenance, automated validations, and continuous monitoring of system behavior.
A central contribution of ETSI EN 304 223 is its lifecycle approach. Security does not begin in testing nor end at go live. The text assigns obligations from design and acquisition, through development, integration, and deployment, and extends controls into operations, maintenance, and end of life. That continuity reduces the chance of gaps when new data sources are introduced, models are updated, or components are decommissioned.
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The standard also clarifies who does what. It defines actors with distinct responsibilities that form the basis for governance in real environments. Developers build and validate models and components, document assumptions and limits, and provide evaluation artifacts and traceability. System Operators govern the runtime and environment that execute the system, from infrastructure and platforms to observability, incident response, and patching. Data Custodians manage data lifecycle, quality, authorized use, and protection of sensitive information. This operational split streamlines audits, delineates legal obligations, and creates measurable control points.
The supply chain dimension receives explicit treatment. Models, libraries, datasets, and third party services must pass a verification process that proves integrity, origin, and maintenance, with up to date inventories, signing, and update controls. In AI, dependence on toolchains and external repositories multiplies risk if there is no procurement and hardening policy that covers both software artifacts and hardware accelerators with their drivers.
For adopting enterprises, the practical impact shows up in three layers. First, design and architecture. You need a security layer across the stack with strong authentication, role based authorization, tenant separation, and privacy policies embedded in data flows. Second, measurable operations. The standard pushes teams to instrument metrics for accuracy, drift, bias, and abuse with thresholds and remediation plans. Third, effective governance. Decision making should record model versions, prompts, knowledge sources, and exceptions so that compliance and audit can reconstruct any automated decision.
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Moving from regulatory requirement to production systems means adapting proven reliability and security practices to AI’s context. Static controls must live alongside continuous evaluation of inputs and outputs. Data validation needs quality contracts and provenance checks. Observability cannot stop at infrastructure metrics. It must include signals of model behavior, drift alerts, and controlled degradation paths. On the product side, it is wise to define fallbacks that keep service intact when a model fails to meet required levels.
This is where the clear assignment of roles makes execution easier. Developers do more than train. They deliver test suites, performance reports, and usage limits. System Operators implement logging with data protection, enforce access controls, define maintenance windows, and coordinate incident response. Data Custodians build catalogs, close quality gaps, and audit legitimate use of personal or confidential information. With this machinery in place, compliance stops being a paperwork exercise and becomes a daily routine.
In real programs, many organizations complement internal teams with specialized capabilities. Square Codex operates in that space. Through a nearshore staff augmentation model from Costa Rica, it embeds software engineers, data specialists, and AI teams within North American companies to translate ETSI EN 304 223 into secure architecture, governed pipelines, and traceable deployments. That work includes defining access controls, designing reliable APIs, establishing data catalogs with retention policies, and setting up automated evaluations that measure drift, accuracy, and compliance.
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Square Codex’s contribution continues in day to day operations. Its teams implement compliance oriented MLOps, instrument observability that distinguishes failures in the model, the data, or the integration, and prepare degradation and response plans that keep services under control. With KPI dashboards, per team budgets, and rollback paths, they help security advance alongside the product without slowing delivery. For organizations with regulatory commitments and service level agreements, this bridge between standard and execution reduces risk and speeds time to production.
The desired outcome is AI that is secure by design and verifiable in operation. ETSI EN 304 223 provides the framework and shared language to achieve it. Applied with discipline, it aligns strategy, engineering, and data governance, turning AI adoption into sustainable capabilities that withstand audits, incidents, and demand shifts without sacrificing speed or business value.