{"id":6888,"date":"2026-10-14T09:38:54","date_gmt":"2026-10-14T12:38:54","guid":{"rendered":"https:\/\/wearebigcheese.com\/sin-categorizar\/govern-ai-before-scaling-it-people-agents-and-tools\/"},"modified":"2026-10-14T11:20:46","modified_gmt":"2026-10-14T14:20:46","slug":"govern-ai-before-scaling-it-people-agents-and-tools","status":"publish","type":"post","link":"https:\/\/wearebigcheese.com\/en\/blog-en\/govern-ai-before-scaling-it-people-agents-and-tools\/","title":{"rendered":"Govern AI Before Scaling It: People, Agents, and Tools"},"content":{"rendered":"\n

Govern AI Before Scaling It: People, Agents, and Tools<\/h2>\n\n

In organizations that are rapidly adopting generative AI\u2014and especially in fintech and payments, where PCI DSS<\/a> and financial regulations are non-negotiable\u2014a very similar story is unfolding. First, each team builds its own assistant, support agent, or automation tool, and before long, no one has a complete inventory of which agents exist or what they can do. Many of these agents inherit broad permissions from the application that hosts them, or use shared credentials to access APIs and tools, so access exists but no one has explicitly defined it. <\/p>\n\n

Added to this is growth. When a company acquires another, it also takes on its cloud accounts, its identity provider, and its own compliance standards\u2014and what was once a segmented environment now becomes a series of silos. New needs also arise, such as taking an agent designed for end customers and offering it to B2B clients or partners as well, which requires separating data and permissions between customers. Meanwhile, the security team needs a comprehensive view of the organization\u2019s security posture\u2014a view that, in most cases, is still put together manually. <\/p>\n\n

The bottom line is that AI governance is, above all, an issue of identity and access. It does not require the invention of new disciplines: it is solved using the same principles we already apply in the cloud\u2014such as a single identity plane, least privilege, preventive controls, and centralized auditing\u2014now extended to a new type of actor. <\/p>\n\n

But it\u2019s not just a matter of security. A production agent also places an additional burden on the platform: it consumes models and computing resources, depends on integrations with other systems, and can degrade critical services if it isn\u2019t well designed. That\u2019s why government operations run more smoothly when Security and IT work together as validators and reviewers of what the teams build: Security oversees access and data, while IT manages the cost, performance, stability, and quality of the integrations each agent requires. But how does all of this scale without Security and IT becoming a bottleneck? <\/p>\n\n

\"\"<\/figure>\n\n

Five-Layer Framework<\/h4>\n\n

Layer 1 \u2014 A multi-account foundation that accommodates growth<\/p>\n\n

It all starts with organizing where workloads reside. An AWS organization managed according to the Well-Architected Framework allows accounts to be grouped by company or business unit and separated by environment, so that each new acquisition is integrated following a standard process rather than remaining an isolated entity with its own set of rules. Within that structure, non-negotiable limits are defined\u2014such as permitted regions, which AI services can be used, or the requirement that auditing cannot be disabled\u2014and these limits are proactively enforced across the entire organization. <\/p>\n\n

This layer is also the best tool for narrowing the scope of compliance. If the workloads that process card data reside on dedicated accounts, the scope of a PCI audit is limited to those accounts, and the rest of the organization can innovate more freely. <\/p>\n\n

Layer 2 \u2014 A single point of entry for people<\/p>\n\n

User identities should be managed in a single location. The corporate identity provider\u2014whatever it may be\u2014is federated (for example, with IAM Identity Center), so adding or removing a user only needs to be done once and is reflected across all accounts and tools. Permissions are assigned by role and by group, never on an individual basis. <\/p>\n\n

The key is for AI tools for business to come through the same door. If an assistant like Amazon Quick uses that same identity, permissions for spaces, data, and agents are derived from existing groups, and the security team governs the AI using the same model it uses to govern everything else. <\/p>\n\n

Layer 3 \u2014 Agents are also identities<\/p>\n\n

This is the most important shift in mindset. An agent that queries a database, calls an API, or invokes a tool through MCP is not an invisible extension of the application: it is an actor that requires its own identity, minimal permissions, and a record of its actions. Furthermore, when an agent acts on behalf of a user, it should carry that user\u2019s context with it, so that it can never access anything the user would not be able to see. <\/p>\n\n

In practice, this is solved using a common platform to host the agents\u2014such as Amazon Bedrock AgentCore<\/a>\u2014which assigns them identities, stores their credentials for external services without hard-coding them, and centralizes access to tools through a gateway. Instead of each agent connecting directly to each MCP, they all pass through a control point where the caller is authenticated and their permissions are verified. As a result, the question \u201cWhich agent can invoke which tool, and on whose behalf?\u201d is answered by looking at a configuration, not by reading code. <\/p>\n\n

The same principle resolves the case of an agent shared among multiple clients. If each client\u2019s identifier is embedded in the user\u2019s identity and the tools filter the data on the backend, separation is guaranteed by the access layer and does not depend on the model following an instruction in the prompt. <\/p>\n\n

Layer 4 \u2014 Constraints on Model Behavior<\/p>\n\n

Identity defines what an agent can access; guardrails define what the agent can say and do with that information. Filtering out prohibited topics, detecting and masking personal data in inputs and outputs, and adjusting those controls based on the use case\u2014with stricter controls for an agent speaking with an end customer than for an internal one\u2014is what enables us to deploy AI with confidence. Bedrock Guardrails<\/a> allows you to define these policies once and apply them to different models and agents. <\/p>\n\n

The other half of this layer involves keeping sensitive data out of the model\u2019s reach. Tokenizing card or account numbers before they reach the agent or are used to train a model reduces the risk of exposure without losing value: consistent tokenization\u2014where the same value always generates the same token\u2014still allows for grouping transactions or detecting anomalous behavior without anyone seeing the actual data. <\/p>\n\n

Layer 5 \u2014 Visibility for Security and IT<\/p>\n\n

None of the above is useful if it cannot be audited. A centralized log of all actions\u2014including model invocations and what each agent does\u2014makes it possible to reconstruct what happened, on whose behalf, and with what data. Building on that foundation, consolidating security findings and compliance controls from all accounts in one place\u2014for example, using Security Hub and Security Lake<\/a>\u2014gives the security team the big-picture view that they currently have to piece together manually. <\/p>\n\n

The same logic applies to IT. Measuring latency, errors, and token consumption for each agent, and allocating costs by team or business unit through resource tagging, makes it possible to detect early on an agent that has become expensive, slow, or unstable, or an integration that is becoming overloaded. Agent observability\u2014using tools such as CloudWatch and AgentCore Observability\u2014along with cost management, is just as much a part of governance as access auditing. <\/p>\n\n

Presenting that information on a dashboard that shows the status by company and by account changes the conversation with management: governance goes from being a vague concept to becoming a metric that can be tracked over time.<\/p>\n\n

Checklist: Seven Questions Security and IT Should Be Able to Answer<\/h4>\n\n
    \n
  1. Do we have an inventory<\/strong> of all agents in production, including their owners and purposes?<\/li>\n\n\n\n
  2. Does each agent have its own identity<\/strong> and minimum permissions, or does it inherit them from its application?<\/li>\n\n\n\n
  3. Do agents access tools and MCPs through an auditable access point<\/strong>?<\/li>\n\n\n\n
  4. Do user onboarding and offboarding take place within a single IdP<\/strong> that also manages the AI tools?<\/li>\n\n\n\n
  5. Can we reconstruct what an agent did, on whose behalf, and using what data<\/strong>?<\/li>\n\n\n\n
  6. Does every agent have a business metric<\/strong> that justifies their cost?<\/li>\n\n\n\n
  7. Do we know how much<\/strong> each agent and its integrations cost, how they perform, how stable they are<\/strong>, and who is responsible if they experience a service degradation?<\/li>\n<\/ol>\n\n

    If any answer is “I don’t know,” that’s the starting point.

    You can find other     action-oriented questions here<\/a>. <\/p>\n\n

    Government as an Enabler<\/h4>\n\n

    A common mistake is to think that governance means restriction. Experience shows us the opposite: the organizations that put AI into the hands of the business the fastest are the ones that first addressed identity, guardrails, auditing, and observability. Once the framework is in place, Security and IT can say \u201cyes\u201d to new use cases without having to evaluate each one from scratch. The challenge is to establish a framework for all of this while the business continues to move forward. <\/p>\n\n

    And while we\u2019re focusing on the technical side here, the same rule applies to every initiative: no feature should go into production without a business outcome and a metric to measure it. Effective leadership also means knowing what value each feature brings. <\/p>\n\n

    \n
    <\/div>

    Sebasti\u00e1n Grattarola<\/p>

    CTO de BigCheese, premier partner de AWS. Ayudo a fintechs, retails, y empresas de pagos de LATAM a construir en AWS con gobierno, seguridad y escala.<\/p><\/div><\/div>

    Read MoreGovern AI Before Scaling It: People, Agents, and Tools<\/span><\/a><\/div>\n","protected":false},"author":7,"featured_media":6886,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","neve_meta_reading_time":"","_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[91,1],"tags":[],"class_list":["post-6888","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en","category-sin-categorizar"],"yoast_head":"\nGovern AI Before Scaling It: People, Agents, and Tools - BigCheese, AWS Premier Partner<\/title>\n<meta name=\"description\" content=\"AI Agent Management on AWS: A Five-Layer Reference Architecture for Providing Identity, Access Control, and Auditing for People and Agents.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wearebigcheese.com\/en\/blog-en\/govern-ai-before-scaling-it-people-agents-and-tools\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Govern AI Before Scaling It: People, Agents, and Tools - 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