TL;DR
An enterprise LLM is a large language model run under business controls for private data, governed access and traceable answers. The model is often the same one consumers use. What changes is the contract, the deployment and the system built around it. Seven requirements make an LLM enterprise-grade, namely privacy, security, reliability, integration, governance, auditability and cost control. Teams should apply pass/fail gates first, then score the finalists on their own test data. Rollout works best one workflow at a time, expanding only after each clears agreed production checks.
Key Takeaways An enterprise LLM is usually a familiar model wrapped in enterprise terms, deployment choices and controls, so the difference lies outside the model. Seven requirements make it enterprise-grade, covering data privacy, security, reliability, integration, governance, auditability and cost control. Vendor API, cloud-hosted, private and on-prem deployments trade speed for control, and many large companies end up running more than one. Pass/fail gates on data use, residency and access should remove candidates before any weighted scoring starts. Test models on your own documents and tasks, and measure cost per accepted answer instead of price per token. Roll out one workflow at a time, with acceptance criteria and a rollback plan agreed before the pilot begins. Watch on YouTube
Enterprise AI Adoption: Why Employees Resist and Most AI Projects Fail
Kanerika looks at why enterprise AI projects stall between a promising start and real use, from employee resistance to missing groundwork. It is the same gap that parks an LLM pilot when the wider business is not ready for it.
The Pilot Worked. Then Legal and Security Said No. The demo went well. A team pointed a popular model at a folder of supplier contracts, asked it pointed questions and got sharp answers in seconds. Six weeks later, though, the project was parked.
Legal wanted to know whether the vendor could keep those contracts or train on them. Security then asked who could see which documents, and where the prompts were logged. Finance asked what the tool would cost at ten thousand questions a day, but nobody had a number.
Gartner saw this pattern early. In July 2024 it predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. The causes it named were poor data quality, inadequate risk controls, escalating costs or unclear business value.
None of those reasons is about how clever the model is. Instead, they are enterprise questions. Answering them before the pilot is what turns a promising model into an enterprise LLM the business will sign off.
Our AI implementation roadmap covers the wider program, while this guide stays on the LLM itself.
What Is an Enterprise LLM? An enterprise LLM is a large language model that a company runs under its own rules. Those rules cover where data goes, who can use the model, how answers get checked and what the service must deliver. The model underneath usually comes from a familiar family, and our guide to the top LLMs compares those models by use case.
Adoption is no longer the open question, since Stanford’s 2026 AI Index reports that organizational AI adoption reached 88% . The harder question is which of those deployments can survive an audit, a security review and a budget cycle.
Enterprise LLM vs Consumer AI Chat The gap between a consumer chat app and a company deployment shows up in the terms and the plumbing, far more than in the model. So the table compares them on the points a review board actually checks.
Dimension Consumer AI chat Enterprise deployment Use of your data for training May improve the provider’s models unless the user opts out Excluded by contract and written into the agreement Identity and access Personal accounts Single sign-on, roles and group-based permissions Data location Wherever the provider chooses A chosen region or your own environment Logging and audit Chat history for one user Central logs of prompts, sources, outputs and model versions Service terms Best effort Uptime commitments, support tiers and an incident process Integration Copy and paste APIs and connectors into ERP, CRM and document stores Cost model Flat monthly fee per person Usage-based spend, budgeted by workload, with hard limits
When you read down the right-hand column, a pattern appears. Every row is something a CIO, a CISO or a general counsel will ask about, but none of them is a model benchmark.
The Model Is Only Part of the System Buyers often ask whether a model is enterprise-ready, but that question splits in two. Some requirements belong to the provider, such as data-use terms, regional hosting and uptime commitments.
Others belong to what your team builds around the model. Permission-aware retrieval, logging, evaluation, spend limits and human review all live in your application, so no vendor contract supplies them. For example, a model with perfect terms can still surface a salary file when the search index behind it ignores document permissions.
Keeping that split in mind prevents a common mistake, which is buying an enterprise license and assuming the job is done. That is also why the LLM architecture inside the model matters less here than the system you wrap around it.
What Makes an LLM Enterprise-Grade? Seven Requirements Seven requirements separate an enterprise-grade deployment from a clever prototype. Each one comes with a test you can run or a document you can request. That is what makes the list useful in a review meeting, rather than on a slide.
1. Data Privacy and Residency Privacy starts with one plain question, especially for regulated data. Does the provider keep, read or train on what you send? The major cloud services now answer this in writing, so get that answer before anything else.
Microsoft’s documentation for models sold through Azure, for instance, says your prompts, completions and embeddings are not available to other customers or to OpenAI . It also says they are not used to train foundation models without your permission. Similarly, AWS states that model providers on Bedrock have no access to Bedrock logs or to customer prompts and completions .
Residency is the second half, although it hides in deployment settings. Azure’s global and data zone deployment types keep stored data in your chosen geography. However, they can process prompts elsewhere, so a team with strict residency rules has to pick the deployment type deliberately.
Personal data also adds its own layer of rules on top. Our breakdown of AI privacy risks covers what happens to that data along the way.
2. Security and Access Control In effect, an LLM inherits the reach of everything connected to it. When an assistant can query a document store, it can expose anything that store can read. Access therefore has to be enforced at retrieval time, using each user’s own permissions.
The attack surface is also new. The OWASP Top 10 for LLM applications puts prompt injection first and sensitive information disclosure second in its 2025 list. So an enterprise-grade setup needs single sign-on, role-based access, encryption with keys you control and tested defenses against injected instructions.
Controls at that depth deserve their own plan, and our guide to generative AI security controls lays out seven layers. For the threat side, the LLM security guide walks through the risks one by one.
On-Demand Webinar
The Real Cost of LLM Security Risks and How to Reduce Them
Prompt injection and data leakage carry a real price once an LLM touches company data. Kanerika’s on-demand session covers the financial impact of LLM security risks and the guardrails that reduce them.
Watch the Webinar → 3. Reliability, Latency and SLAs A help desk assistant that times out at 9 a.m. on Monday will be abandoned by Tuesday. Reliability means uptime commitments in the contract, response times that hold at peak load and a clear path when the provider has an incident.
Ask for the service level agreement in writing, and then read what it excludes. After that, run your own load test, because 95th-percentile latency under real traffic tells you more than any brochure figure.
Reliability also covers the answers, since a model that invents a policy clause is unreliable even when the endpoint is up. That is why grounding and evaluation matter as much as uptime. Our piece on LLM hallucination on enterprise data explains where wrong answers come from.
One more reliability question gets missed in most contracts. Providers retire model versions on their own schedule, so ask how much notice you get and whether you can pin a version. A forced upgrade can change answers overnight, and only your own test set will show by how much.
4. Integration With Enterprise Systems Value shows up when the model works inside the tools people already use. In practice that means connectors to the CRM, the ERP, the data warehouse and the document stores. It also means identity that flows from the same directory as everything else.
Integration is also where most of the build effort goes. A model call takes a few lines of code, while getting the right data to it, with the right permissions and format, takes weeks. Teams comparing broader platforms can also use our enterprise AI platform evaluation guide for that wider decision.
Case Study
90% Fewer Data Query Errors with Microsoft Copilot
Kanerika deployed Microsoft Copilot and Power Automate for a California dairy’s sales team, reachable through Teams and an intranet chatbot and connected to Azure SQL. Data query errors fell 90% and decision-making became 30% faster.
Read the Case Study → 5. Governance and Regulatory Fit Governance answers who may use the model, for what and with which data. The starting point is a written policy, so a generative AI policy should set out allowed uses, banned uses and the approvals in between.
Regulation, on the other hand, sets the outer edge. The EU AI Act has applied obligations to general-purpose AI models since 2 August 2025 . Its rules for high-risk uses in sensitive areas then follow on 2 December 2027.
In the US, many teams map their controls to the NIST Generative AI Profile, NIST AI 600-1 , published in July 2024. Still, policy only works when someone owns it. Our AI governance framework covers the operating model, while the guide to AI compliance maps regulations to controls.
6. Auditability and Traceability When an answer goes wrong, someone will ask how it happened. An auditable deployment can show the prompt, the documents retrieved, the model and version, the output and who saw it.
That record earns its keep three times over. It supports incident review and gives internal audit the evidence it asks for. It also lets your team compare model versions before switching.
Logging has to be designed in from day one, because retrofitting traces into a live assistant is slow and usually incomplete. Production tracing is a discipline of its own, and our comparison of LLMOps observability tools covers the tooling.
Watch on YouTube
AI Audit Framework: EU AI Act, NIST, ISO 42001 & the 6-Step Blueprint
Auditability is easier to design in than to bolt on. This video walks through an AI audit framework built on the EU AI Act, NIST and ISO 42001, with a six-step blueprint for checking AI before risk becomes a problem.
7. Cost Control and Predictability Token prices fall quickly, at least for the same level of capability. Stanford’s 2025 AI Index found that the inference cost for a system performing at GPT-3.5 level dropped over 280-fold between November 2022 and October 2024 . Bills still surprise people, because usage grows faster than prices fall.
The useful metric for an enterprise LLM is cost per accepted answer. It counts tokens, retries, retrieval calls and the human review time needed before an output can be used. So a cheaper model that needs twice the review is not really cheaper.
Spend limits per team, caching for repeated questions and routing simple requests to smaller models all keep the bill predictable. An LLM gateway is the usual place to enforce those rules. Our generative AI ROI benchmarks then show how to tie the spend to returns.
Enterprise LLM Deployment Models: Vendor API, Cloud-Hosted, Private and On-Prem Where the model runs decides how much of the list above you control directly. There are four broad options, and the right one depends mostly on how sensitive the data is and who will run the thing.
Deployment model Where the model runs Control over data Effort and cost shape Best fit Vendor API The model provider’s cloud Contract terms only Days to start, pay per token Low-sensitivity data and fast experiments Cloud-hosted model service Your cloud provider’s AI service, in a region you pick Contract plus your own network, key and logging controls Weeks to start, per token or reserved capacity Regulated workloads already on that cloud Private deployment in your cloud An open-weight model on compute you manage Full Weeks to months, GPUs paid for whether used or not Strict data rules with steady, high volume On-premises Your own data center Full, including air-gapped setups Months, hardware plus specialist staff Sovereign, classified or disconnected environments
Vendor API Calling a provider’s API directly is the fastest way to start, especially for a first experiment. The trade-off is that your controls end at the contract, so data-use terms, retention settings and regional options have to be confirmed in writing first.
Cloud-Hosted Model Service Many enterprises land here for production. The same frontier models run inside the cloud account you already govern. You also get private networking, your own encryption keys and logs that flow into existing security tools.
Private Deployment in Your Cloud Running an open-weight model on your own cloud compute gives full control over weights, data and updates. In return, you take on capacity planning, patching and model upgrades, which needs a team that can run GPU infrastructure. Our guide to open-source LLMs worth evaluating covers the candidates.
On-Premises On-prem suits environments that cannot send data to any cloud at all. It brings the most control as well as the highest cost of ownership, and the private LLMs guide covers that route in depth.
Before committing to either private route, check whether a smaller model would do the job. Our comparison of SLMs vs LLMs shows where compact models hold up and where they fall short.
Hybrid and Multi-Model Routing In general, few large companies stay with one option. A common pattern sends confidential workloads to a private or cloud-hosted model and routes general drafting to a vendor API. Meanwhile a smaller model handles high-volume classification.
A gateway in front of the models applies the same identity, logging and spend rules to every route. It also turns a provider switch into a configuration change rather than a rebuild, which matters when model versions retire every year. For teams weighing two providers side by side, our look at OpenAI vs Anthropic for enterprise fit is a useful starting point.
Matching the Deployment Model to Your Constraints Five questions narrow the choice quickly, so start there. Work through them in order, because an early answer often settles the rest.
Data classification. Can this data leave your environment under a signed agreement?Residency. Must prompts be processed, and not only stored, inside one country or region?Volume. Is demand steady enough that reserved capacity or your own GPUs cost less than paying per token?Operating capacity. Do you have people who can patch, scale and upgrade model infrastructure every quarter?Latency. Does the use case need answers in under a second, even at a site with limited connectivity?Suppose the answers are yes to the first, no to the second and no to the fourth. That combination points to a cloud-hosted model service, which is why it is a common production choice.
Customizing the Model: Prompting, RAG or Fine-Tuning Most business questions depend on company knowledge the model never saw during training. Three methods close that gap, and they stack rather than compete.
Prompting with clear instructions and examples comes first, since it costs nothing to try. Retrieval-augmented generation comes next, because it pulls current documents into each request so answers can cite their sources.
Fine-tuning changes the model’s behavior or output format, so it is worth the effort only for narrow, repeated tasks. Our guide to RAG vs fine-tuning sets out the trade-offs in detail.
How to Choose an Enterprise LLM: A Weighted Scorecard Public leaderboards measure general skill, but only on public tests. Your decision, however, depends on how a model handles your documents, your rules and your volume. A scorecard turns that into evidence a committee can approve.
Step 1: Write Down the Workload To begin with, name the task, the inputs, the expected output and the cost of an error. A model that summarizes meeting notes and one that drafts loan decisions face very different bars, even though both are writing text.
Step 2: Apply Pass/Fail Gates First Some requirements are not trade-offs at all. Data-use terms, residency, required certifications, access integration and licensing either meet your rules or they do not.
So run these gates before any scoring. A model that fails one is out, however well it writes. This single step saves weeks of evaluation on candidates that legal would have rejected anyway.
AI Assessment
Is Your Organization Ready for an Enterprise LLM?
Pass/fail gates only work when you know where your data, controls and teams stand today. Kanerika’s 6-minute AI Maturity Assessment evaluates your readiness across AI/ML foundations, generative AI capabilities and AI agent deployment.
Start Your AI Assessment → Step 3: Build a Test Set From Real Work Collect a few hundred real examples, anonymized where needed, especially the awkward ones. Edge cases, ambiguous requests and known failure scenarios matter more than easy questions.
Agree on how answers will be graded before testing begins, so the result cannot be argued after the fact. Our LLM evaluation framework guide also covers metrics and grading methods in detail.
Step 4: Score the Finalists Score each surviving model from 1 to 5 on every criterion, multiply by the weight and then add the results. The weights below suit a document-heavy workflow, while the scores for candidates A, B and C are illustrative rather than real benchmark results.
Criterion Weight Evidence to collect A B C Task accuracy on your test set 30% Pass rate on real cases graded by reviewers 4 5 3 Data handling and risk 20% Signed data-use terms, residency and retention settings 5 4 4 Reliability and latency 15% 95th-percentile latency and error rate under load 4 3 5 Cost per accepted answer 15% Tokens, retries and review time per approved output 3 2 5 Integration fit 10% Works with your identity, data and application stack 5 4 3 Lifecycle and support 10% Version pinning, retirement notice and support terms 4 4 3 Weighted score (out of 5) 100% Sum of each score multiplied by its weight 4.15 3.85 3.80
Candidate B writes the best answers yet finishes second, because it gives back more than that lead on cost, latency and data handling. Candidate C is cheapest and fastest but too weak on accuracy for this workload. That is the whole point of weighting, since it forces the trade-off into the open.
Step 5: Run a Production-Like Pilot Take the top one or two models into a pilot with real users, real data and identical prompts, limits and review rules. Then record what you chose, what you rejected and why. That record is what you will reopen when the next model version arrives.
Enterprise LLM Use Cases by Business Function Use cases tend to cluster by department, and so do the risks. For each one, the useful question is which requirement will decide whether it reaches production.
Function Typical LLM use case Requirement that usually decides it Customer service Draft replies, summarize tickets, answer from the knowledge base Reliability, with handoff to a person when confidence is low Legal and procurement Contract review, clause extraction, vendor agreement questions Privacy and an audit trail for every answer Finance Variance commentary, policy questions, invoice exception notes Accuracy on numbers and traceable sources HR Policy assistant, job description drafts, onboarding questions Access control so personal records stay scoped Sales and marketing Account research, proposal drafts, plain-English questions on CRM data Integration with the CRM and the data warehouse IT and engineering Code assistance, incident summaries, runbook search Security of source code and secrets Operations Procedure search, shift handover summaries, supplier email triage Integration with ERP and latency on the floor Knowledge management Enterprise search across documents, with citations Permission-aware retrieval
Where to Start Two of these rows tend to pay back first. AI for customer service has high volume and measurable handle time. Likewise, AI contract analysis replaces slow manual reading with answers a reviewer can check against the source clause.
Document-heavy work in finance and operations usually starts with extraction, which our guide to intelligent document processing covers. Company-wide search is a bigger project, so the piece on AI knowledge management explains how to keep it from exposing what it should not.
Kanerika Service
Generative AI Services
From customer service assistants to contract review and enterprise search, Kanerika designs and builds generative AI use cases with the access, logging and cost controls that production needs.
Explore Generative AI Services How to Roll Out an Enterprise LLM in Six Phases Because each step is small, a phased rollout limits the blast radius of every mistake. Each phase ends with a decision, and nobody moves on until the evidence supports it.
Pick one workflow and measure it. Choose a task with clear volume, a current cost and an owner who wants it fixed. Then record today’s time, error rate and cost so the pilot has a baseline.Clear data and deployment approvals. Confirm which data the model may see, which deployment model applies and who signs off. Legal and security join here, before the demo rather than after it.Run a controlled evaluation. Apply the gates and the scorecard to two or three candidates. Reject anything that fails a gate, even if it looked impressive.Launch a limited pilot. Give the selected setup to a defined group with real tasks, human review and full logging. Track adoption as closely as accuracy, because unused tools fail quietly.Pass the production gates. Check task success, critical error rate, latency, cost per accepted answer and support readiness against thresholds agreed in phase one.Expand and keep testing. Scale by workload or business unit, and rerun the test set after every model or prompt change. Also keep a rollback path open.The phases look slow on paper but usually save time. A pilot that clears approvals in phase two never stalls in week six, which is exactly where the contract assistant in our opening story died.
Checklist
Generative AI Checklist for Secure Adoption and Governance
Approvals, data rules and acceptance criteria are easy to miss when a pilot is moving fast. Kanerika’s downloadable checklist helps teams confirm the basics before an LLM goes into production.
Get the Checklist → What to Measure Once It Is Live Accuracy on launch day is a starting point rather than a result. An enterprise LLM in production needs three kinds of measures, reviewed on a fixed schedule instead of after a complaint.
Technical measures include task success, structured output success, 95th-percentile latency, availability and failed requests. Business measures include time saved per workflow, less manual review, adoption and cost per accepted answer. Risk measures then track critical errors, policy violations, unsupported answers and regressions after a model change.
Agree in advance what triggers a fresh evaluation, before an incident forces the question. A drop in task success, a jump in cost, a new regulation or a retired model version should all send the workload back through the scorecard.
Where Enterprise LLM Rollouts Break Down Failed rollouts rarely fail on model quality. Instead, they fail on decisions made early and checked late, and the same few show up again and again.
Choosing by leaderboard. Public benchmark rank says little about your contracts, tickets or ledgers.Treating enterprise-grade as a label. An enterprise license covers the provider’s side, while permissions, logging and evaluation remain your job.Scoring what should be a gate. Residency and data use are pass/fail, so averaging them into a score hides a blocker.Testing on clean examples. Pilots built on tidy samples break on the first messy scan or ambiguous request.Pricing by the token. Retries, long prompts and review time often cost more than the tokens themselves.Expanding without exit criteria. Without thresholds and a rollback plan, a weak rollout keeps running because nobody can prove it failed.Each of these maps back to one of the seven requirements or one of the rollout phases. That makes the framework useful in practice, because it turns vague worries into checks someone can own and close.
How Kanerika Builds Enterprise LLM Solutions That Clear Review Kanerika builds LLM applications for companies where the review board matters as much as the demo. Our LLM development services start from the same seven requirements in this guide, because those are the questions that stop projects late.
Every engagement follows our six-step IMPACT methodology, which starts by identifying the gaps and mapping the requirements with legal and security in the room. After that, it moves to proving the value, analyzing root causes, creating the solution and transforming at scale. The solution is built with permission-aware retrieval and full logging, and evaluation sets and spend limits are in place before it scales.
A few pitfalls come up often enough that our teams check for them by default. Document permissions in the source system are frequently looser than anyone assumed, so we audit them before indexing anything. Test sets also drift away from real traffic within months, which is why we refresh them from production logs.
Kanerika is an OpenAI Select Partner and builds enterprise solutions on both Anthropic’s Claude and OpenAI’s models. That lets us recommend the right model for each workload instead of defaulting to one vendor. Kanerika also holds ISO 27001 and ISO 27701:2019 certifications and SOC 2 Type II compliance.
Kanerika Results From Real AI Deployments The same review-first approach shows up in our wider AI work. For a California dairy’s sales team, Kanerika deployed Microsoft Copilot with Power Automate, reachable from Teams and an intranet chatbot and connected to Azure SQL. As the case study reports, data query errors fell 90% and decisions came 30% faster.
For a regulatory compliance provider serving financial institutions, we built an AI-powered compliance platform with automated requirement mapping, controls monitoring and audit traceability. Manual compliance work fell 60%, while regulatory response became 40% faster. Teams that need the governance layer on its own can start with our AI governance services .
Case Study
60% Less Manual Compliance Work with AI
Kanerika built an AI-powered compliance platform for a regulatory compliance provider serving financial institutions, automating requirement mapping, controls monitoring and audit traceability. Manual compliance work fell 60% and regulatory response became 40% faster.
Read the Case Study → Wrapping Up An enterprise LLM is defined less by the model than by what surrounds it. Seven requirements decide whether it is enterprise-grade, and most of them live in the contract and the surrounding system rather than in the model. Choose a deployment model that fits your data and your team, then apply pass/fail gates before scoring candidates on your own work.
Finally, roll out one workflow at a time with exit criteria agreed in advance. That way, the questions from legal and security arrive at the start of the project, when they can still be answered.
Frequently Asked Questions
What is an enterprise LLM? An enterprise LLM is a large language model that a company runs under its own rules for data use, access, logging and service levels. The model is often the same one consumers can use. What makes it enterprise is the contract, the deployment choice and the controls built around it, so answers stay private, governed and traceable.
What makes an LLM enterprise-grade? Seven requirements make an LLM deployment enterprise-grade. They are data privacy and residency, security and access control, reliability with service levels, integration with business systems, governance, auditability and cost control. Some come from the provider’s contract and hosting, while others, such as permission-aware retrieval and logging, have to be built by your own team.
How do companies choose the right LLM for enterprise use? Strong selection processes start with the workload, then apply pass/fail gates on data use, residency, access and licensing. Models that survive are tested on a few hundred real examples and scored against weighted criteria such as accuracy, risk, latency, cost per accepted answer and integration. The top one or two then go into a production-like pilot.
What is the difference between cloud-hosted and on-premises enterprise LLMs? A cloud-hosted LLM runs inside a cloud provider’s AI service in a region you choose, with your network, key and logging controls around it. An on-premises LLM runs on hardware in your own data center. On-prem gives full control and suits disconnected environments, but it costs more and needs specialist staff to operate and upgrade.
How much does an enterprise LLM deployment cost? Cost depends on volume, deployment model and how much review each answer needs. Vendor APIs and cloud services charge per token or for reserved capacity, while private deployments pay for compute whether it is used or not. Integration, evaluation, monitoring and human review often cost more than tokens, so budget by cost per accepted answer.
How long does it take to deploy an enterprise LLM? A single well-scoped workflow on a cloud-hosted model can reach a limited pilot in weeks once data and security approvals are clear. Private and on-premises deployments take longer because infrastructure has to be built first. Data readiness, approval cycles and integration work usually set the timeline more than the model itself does.
Do enterprise LLM providers train on company data? Major cloud services state in their terms that business prompts and outputs are not used to train foundation models without permission. Microsoft documents this for models sold through Azure, and AWS says model providers on Bedrock cannot see customer prompts. Confirm the exact terms, retention settings and abuse-monitoring rules for your contract before sending sensitive data.
Should an enterprise use one LLM or several? Many large companies end up using several. Confidential work may run on a private or cloud-hosted model, general drafting on a vendor API and high-volume classification on a smaller model. A gateway in front of all of them keeps identity, logging and spend rules consistent, and makes switching models a configuration change rather than a rebuild.
Is RAG or fine-tuning better for an enterprise LLM? For most business questions, retrieval-augmented generation is the better first step because it brings current company documents into each answer and allows citations. Fine-tuning changes how a model behaves or formats output, and it pays off only for narrow, repeated tasks. Many teams combine clear prompting, retrieval and occasional fine-tuning rather than choosing one.
Do you need a data science team to run an enterprise LLM? Not always. Teams using a vendor API or a cloud-hosted model service mostly need application engineers, data engineers and someone who owns evaluation. Private and on-premises deployments are different, since they need people who can run GPU infrastructure, manage model upgrades and tune performance. Governance and security owners are needed in every case.
How does the EU AI Act affect enterprise LLM use? The EU AI Act has applied obligations to general-purpose AI model providers since August 2025, and rules for high-risk uses in sensitive areas follow in December 2027. Companies deploying an LLM in hiring, credit or similar decisions should expect risk management, documentation, human oversight and logging duties, so auditability needs to be built in early.
