{"id":34510,"date":"2026-07-28T13:08:00","date_gmt":"2026-07-28T11:08:00","guid":{"rendered":"https:\/\/askme.it\/insights\/adopting-genai-without-risk-the-personas-and-landing-zone-method\/"},"modified":"2026-03-26T12:23:20","modified_gmt":"2026-03-26T11:23:20","slug":"adopting-genai-without-risk-the-personas-and-landing-zone-method","status":"publish","type":"insights","link":"https:\/\/askme.it\/en\/insights\/adopting-genai-without-risk-the-personas-and-landing-zone-method\/","title":{"rendered":"Adopting GenAI Without Risk: The Personas and Landing Zone Method"},"content":{"rendered":"
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More than 60% of infrastructure and IT operations leaders are already investing in AI use cases and planning to increase their investments. The problem is that disorderly adoption of GenAI tools accessible to everyone leads to fragmented and unmanageable implementations if IT does not offer timely and structured solutions. Standardizing everything no longer works: the versatility of models makes a one-size-fits-all approach impractical. A different method is needed.<\/p>\n<\/section>\n

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Risk personas: differentiating by risk profile<\/h2>\n

The starting point is to stop applying the same rules to all users and move to a risk-based persona system. Each persona is evaluated across two dimensions: the sensitivity of the data they input into GenAI tools, which includes proprietary IP and customer data, and the sensitivity of the generated outputs, which depends on how they are used in terms of external communication and impact on production systems.<\/p>\n

Evaluating each persona requires answering concrete questions: is the data this user inputs into GenAI tools subject to restrictions under the data classification policy? What would happen if that data became public? Are the generated outputs used directly in external communications? How critical is an output error for business operations?<\/p>\n

The result is a map that enables prioritizing low-risk personas for short-term improvement initiatives, and working with enterprise architecture functions to identify high-value opportunities in riskier areas, exploring mitigations such as synthetic data or private infrastructure.<\/p>\n<\/section>\n

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Cloud landing zones: secure environments for experimentation<\/h2>\n

GenAI adoption requires a structured cloud approach. Even when not using external GenAI services, traditional data center infrastructure alone cannot guarantee the resilience and adaptability required. A disorganized cloud — with separate accounts, different integration techniques, and expenses scattered across credit cards from various teams — becomes a bottleneck for the AI ambitions of the entire organization.<\/p>\n

The cloud landing zone architecture solves this problem: it provides blueprints that embed security, consistency, agility, and autonomy into cloud environments as they scale. With automation tools and provisioning templates, both technical and non-technical users can request an environment suitable for GenAI experiments and receive it quickly, as if using an internal service catalog.<\/p>\n

A practical example: providing synthetic or redacted data as part of a GenAI environment built on a cloud landing zone allows teams to conduct proofs of concept without exposing real data to breach risks or regulatory violations. This keeps the focus on use case validation and development requirements, producing useful inputs for the long-term infrastructure roadmap.<\/p>\n<\/section>\n

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Planning infrastructure from the business case<\/h2>\n

A common mistake is not including the necessary infrastructure resources in GenAI business cases from the start. Requirements vary significantly depending on the deployment model chosen: consuming GenAI embedded in existing applications requires network capacity and integrations with hybrid infrastructure; extending models with RAG or fine-tuning adds vector databases and embedding models; building custom models from scratch requires storage, middleware, and distributed cloud services at scale. Planning these costs in advance, in collaboration with GenAI initiative sponsors, prevents infrastructure from becoming the limiting factor in the corporate AI strategy.<\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Adopting GenAI without exposing sensitive data is possible with a structured approach based on risk personas and cloud landing zones. A practical guide for infrastructure and IT operations leaders.<\/p>\n","protected":false},"featured_media":34512,"menu_order":0,"template":"","insights_category":[555],"insights_tags":[605,665,725,843,847],"class_list":["post-34510","insights","type-insights","status-publish","has-post-thumbnail","hentry","insights_category-ai-and-infrastructure","insights_tags-ai-infrastructure","insights_tags-cloud-landing-zone-en","insights_tags-genai-en","insights_tags-risk-personas-en","insights_tags-security"],"acf":[],"_links":{"self":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34510","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights"}],"about":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/types\/insights"}],"version-history":[{"count":1,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34510\/revisions"}],"predecessor-version":[{"id":34511,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34510\/revisions\/34511"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media\/34512"}],"wp:attachment":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media?parent=34510"}],"wp:term":[{"taxonomy":"insights_category","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_category?post=34510"},{"taxonomy":"insights_tags","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_tags?post=34510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}