AI is no longer confined to pilots. It is increasingly embedded into customer journeys, engineering workflows, and operational decision-making. As adoption accelerates, regulatory expectations and stakeholder scrutiny are rising in parallel, from privacy obligations such as GDPR, CCPA, and APPI, to emerging AI-specific regimes such as the EU AI Act. In this environment, governance becomes the operating system that lets enterprises scale AI safely across markets, teams, and high-impact use cases.

FPT’s governance stack is designed to standardize safety checks, lifecycle discipline, and audit-ready monitoring so responsible AI becomes repeatable at speed. With an AI ecosystem of more than 70 products deployed across 15 countries, plus a talent base of over 25,000 AI-augmented, globally certified engineers, and extensive AI curriculum across its education system, FPT is positioned to operationalize governance as an enterprise operating model rather than a project-by-project obligation.

Since most AI governance remains principle-led guidance and breaks down when models meet real data, users, and operational pressure, FPT’s approach starts from the opposite premise, in which governance is treated as part of the AI delivery stack across four core pillars.

AI Policy & Ethical Guidelines

FPT’s governance policies are aligned to ISO and management-system thinking, including ISO/IEC 42001-style structures, and integrate widely-adopted responsible AI principles such as transparency, robustness, and human-centered design. These policies are operationalized for global delivery realities, including cross-border compliance requirements and explicit authorization controls in ODC environments where data access, model usage, and tool permissions must be strictly governed.

This policy layer is designed to withstand real deployment pressure. High-throughput environments such as the AI Factories in Vietnam and Japan raise the bar for data residency, access control, and traceability. In 2025, the factories launched 43 AI services across the lifecycle, processed 1,111 billion tokens, and expanded to more than 70 models, while new tooling like AI Notebook enabled over 400 labs in a single month, underpinned by residency-bound environments and traceability from training through deployment.

Strategic partnerships, from major technology leaders such as NVIDIA, Microsoft, and SAP to sovereign AI collaborations like those with Sumitomo and SBI Holdings in Japan, further reinforce the need for market-specific guardrails that are clear enough to execute, not just endorse.

 

FPT AI Factories in Vietnam and Japan, equipped with NVIDIA GPU H100 & H200, are ranked among the world’s top 40 fastest supercomputers (according to TOP500 List)

Policy maturity is also sharpened through ecosystem participation, such as the AI Alliance founded by IBM and Meta, the Vietnam Ethical AI Committee, and the Au Lac AI Alliance, keeping guidelines aligned with evolving norms while remaining usable in day-to-day engineering decisions. DevSecOps and ISMS practices then harden these principles into the delivery process, so that privacy, security, and compliance are built into the workflow rather than bolted on at release.

Role, Responsibilities & Training

As policies do not govern AI but people, FPT operationalizes accountability through a defined governance structure that clarifies decision rights across leadership, delivery, engineering, security, legal, and quality functions. This structure establishes who sets standards, who approves high-risk AI use cases, who owns platform controls, and who is responsible for risk acceptance.

FPT’s AI talent pool boasts over 1,000 AI engineers, strengthened by a pipeline of more than 2,000 AI and Data Engineering graduates annually from FPT University

Capability building is therefore treated as a control mechanism. Responsible AI principles and secure AI usage are embedded into training and reinforced through talent programs, which aim to standardize how engineers evaluate risk, handle sensitive data, and apply human validation in high-impact scenarios. 

This is further strengthened through partnerships and initiatives that scale learning and leadership development, from training partnerships with NVIDIA, Landing AI, and Mila Quebec AI Institute to collaboration with the Harvard Business Impact, helping build not only technical competence but decision-making maturity around AI adoption at enterprise scale.

AI-Powered Software Development Life Cycle

AI governance tends to break down when it is treated as a sign-off at the end. FPT addresses this dilemma by embedding governance throughout the AI development lifecycle, utilising structured checkpoints, templates, and human-in-the-loop validation. Two mechanisms that anchor this lifecycle control model include the AI Data Control Gateway, which governs data intake, quality, provenance, privacy handling, and approved usage boundaries, and the AI Model Control Gateway, which governs model selection, evaluation, versioning, documentation, and release gating. 

The company's recent AI platform, FleziPT, further strengthens this pillar by serving as a governance-embedded backbone. FleziPT adoption delivers up to 60% faster development cycles, over 50% less rework, and a 30% productivity uplift, supporting more standardized and traceable workflows across the build-to-release lifecycle.

This discipline translated into measurable operational outcomes, for example, the Insurance360 deployment for a global insurer automated more than 140,000 annual claim requests, enabling 75% of claim requests and 100% of payments to be processed digitally. The solution cut processing time from two days to two minutes and lowered operating costs by 8%, combining AI with deep domain expertise across underwriting and claims.

Standardized components, controlled pipelines, and MLOps and AIOps patterns also help delivery teams operationalize responsible AI consistently across projects, rather than reinventing governance each time.

Audit & Continuous Monitoring

Enterprise AI cannot be governed with one-time checks. Once deployed, systems need ongoing oversight to ensure they remain safe, reliable, and aligned with policy.

FPT’s AI Gateway approach focuses on practical guardrails that reduce misuse and make behavior visible. This includes controls that filter sensitive information, prevent unsafe prompts, limit access, and track how the system is being used. Continuous monitoring and logging then turn governance into evidence, making it easier to investigate issues, report to stakeholders, and meet audit requirements without relying on manual reconstruction.

A clear real-world illustration is FPT’s AI-powered surveillance deployment for a Taiwanese manufacturing leader, where always-on monitoring cut factory safety costs by 80% and reduced manual effort by 70%. The system performs real-time behavior analysis across multiple cameras, keeps workers within designated safe zones, and generates detailed security logs that support audits and incident investigation, using NVIDIA RTX 4080 and NVIDIA NVILA (Vision Language Model) to interpret a wide range of actions beyond predefined rulesets.

 The tech firm recently launched Quantum AI and Cyber Security Institute as a focused unit, reinforcing commitments to tech autonomy and enterprise-grade AI development

As models evolve, data shifts, and regulations tighten, weak governance carries a predictable cost of slower deployments, higher incident risk, and greater compliance and reputational exposure. The organizations that win will not be those with the most ambitious roadmaps, but those that keep AI controlled in production.