What Is LLMOps?

PocketOS is a startup that builds software specifically catering to car rental businesses. In April 2026, it reported that an AI coding agent had deleted their entire production database along with all volume-level backups with a single API call. As complicated as it sounds, later investigations found the entire ordeal took the agent only nine seconds. The agent, supposed to be running a routine task in the staging environment, found an API token with broad privileges. It then used it to issue deletion commands in both staging and production.

The result? Rental companies that had been using PocketOS lost their ability to see their reservations or assign vehicles. The PocketOS team had to spend the subsequent days restoring a three-month-old offsite backup before the infrastructure provider could recover the data.

When all was said and done, the founder laid the blame on the operations around the AI model, and not the model itself. The agent had a credential scoped far beyond its task, and nothing separated staging from production, with backups living in the same environment as the data they’re meant to protect.

Gartner expects this to be a growing nuisance for businesses, with at least half of GenAI projects overrunning their budgets by 2028, owing to poor architectural choices and a lack of operational know-how. It’s the same story from a security perspective, with IBM’s 2026 Cost of a Data Breach report finding that 92% of businesses hit with AI-related breaches lacked proper access controls for their AI systems.

So, what is the solution? LLMOps. LLMOps are a set of practices, tools, and controls organizations can leverage to deploy, monitor, and govern LLMs in production, covering all prompts, retrieval pipelines, agents, costs, and data flowing through it all.

The following guide covers what LLMOps means, how it differs from MLOps, the core stages of LLMOps, and the best practices organizations can adopt to maximize the benefits from LLMOps. Read on to learn more.

LLMOps vs MLOps: What Actually Changes

LLMOps evolves from MLOps as they both share fundamentally the same goal: getting models into production and keeping them safe and reliable. The key difference between the two is scale. While MLOps was originally designed around much smaller models and structured data, LLMOps is capable of dealing with models with billions of parameters that generate open-ended text.

Other than scale, other subtle differences include where they work, their costs, and the risks they address.

With traditional MLOps, a team would own the model end to end. It would collect the labeled data, train the model, validate it, and retrain it for their own purposes. However, most enterprises do not use LLMs that way anymore. They use a foundational model through an API or deploy an open-weight model. They then adapt it through prompts, RAG, and the occasional fine-tuning. Training was the key element of MLOps, which isn’t always part of the equation anymore.

This drastically changes what teams actually operate. Things like prompts, retrieval indices, and agent tool permissions all become production assets with distinct versioning, testing, and control needs. As a result, evaluation is harder, and costs are exponentially different. Gartner’s own report suggests inference will account for almost 70% of a model’s lifetime costs, leading to AI spending becoming an ongoing and usage-driven expense rather than a one-time training investment MLOps was designed to protect.

Additionally, in MLOps, sensitive data risk was specifically concentrated at the training time. This meant that once deployed, a model received narrow and structured inputs. However, in LLMOps, enterprise data flows into the model continuously. Think of a RAG assistant connected to a document repository that can retrieve a confidential file for any employee who structures their prompt properly. The only way to prevent this is to have the retrieval process enforce the permissions of the source as well. This is just one reason why data security and access governance cannot be just an add-on in LLMOps, and must be part of how the pipeline is designed and run.

The LLMOps Lifecycle

LLMOps is not a single tool or a static one-time project. Teams select models, adapt them, connect to enterprise data, shape their behavior through prompts, deploy them into apps and agents, and monitor what happens in production. The typical lifecycle is as follows:

A. Model Selection & Adaptation

Most LLM programs start at the sourcing decision, and not the training run. This means the team is choosing between proprietary models being accessed through APIs, open-weight models they host themselves, or a mix of both. Following on, the weight capability, cost, latency, and how and where the data is processed are all part of the final model evaluation. However, every choice comes with a data decision, as a hosted API sends enterprise data outside the secure internal environment with every request, while fine-tuning embeds data into the model’s weights, where it cannot be removed.

B. Data Pipelines, RAGs, & Vector Stores

In most enterprise LLM use cases, the data pipeline will matter more than the model itself. A RAG pipeline pulls content from all file shares, wikis, ticketing systems, CRMs, and databases, and splits it into chunks, which are then further converted into embeddings to be stored in the vector database. Now, when a user asks a question, the system would retrieve the most relevant chunks and pass them onto the model as contextually necessary. To ensure the pipeline remains accurate means ensuring its sync with the source systems; otherwise, the model outputs will be based on outdated or an overexposed copy of the original data.

C. Prompt Management & Evaluation

System prompts, templates, and examples all define how an LLM is likely to behave. As a result, even the most minor wording changes will cause its outputs to differ greatly. Teams must manage all prompts as versioned assets and measure the effect of every change. All generated text rarely, if ever, has a single correct answer, so evaluation combines several methods such as automated checks for factual grounding, relevance, and policy compliance. This is further complemented by grading using a second model and eventual human review of the sample.

D. Deployment, Guardrails, & Agent Orchestration

Deployment puts the model behind an application, such as an API gateway, a chat interface, a copilot inside the existing tools, or an agent that plans and executes multi-step tasks. Guardrails surround that deployment and filter inputs and outputs for any unsafe content, policy violations, and sensitive data. Agents then add an orchestration layer that decides which tools need ot be called, in what order, and with what credentials. This is the point where the enterprise question shifts from what a model says to what it can do.

E. Monitoring Quality, Cost, & Drift

Once live, LLM applications must be monitored across three main vectors: quality monitoring to track any hallucinations, answer relevance, user feedback, and policy violations; cost monitoring to track token consumption, latency, and spending by application and team; and finally, monitoring drift to ensure providers update their models without breaking the model after these updates, and how users change their interactions after these updates. Any lessons learned can be fed into the start of the lifecycle, allowing for a closed loop in the lifecycle.

Where LLMOps Breaks: The Data Security Gap

While most LLMOps frameworks focus on making models useful, fast, and affordable, far fewer address what happens to sensitive data as it moves through each stage of the aforementioned lifecycle. This is also where most production LLM programs often break, with the gap usually opening in five distinct places:

A. Sensitive Data in Fine-Tuning & RAG Sources

LLMs can always repeat what they were originally given. This means that during fine-tuning datasets that include confidential or sensitive information, that data can find itself part of the outputs as well. Moreover, once encoded as part of the model’s weights, there’s no way to delete it, creating problems as far as data privacy obligations are concerned. This is made exponentially worse when you consider how enterprise repositories are full of overshared, misclassified, and forgotten files. Indexing these resources before cleaning them can turn every sensitive data asset into a resource malicious actors can exploit.

B. Retrieval Bypassing Source Permissions

In the source systems, access controls often determine exactly what systems and entities can open or even find certain files. However, most RAG pipelines often strip those controls away during ingestion. As a result, documents are copied, chunked, and embedded into a shared vector store that returns the most relevant content in response to whatever anyone asks. This leads to privilege escalation where those with inadequate access permissions can theoretically get access to files they would not be able to open directly.

C. Prompt Injection & Data Leakage in Responses

In the OWASP Top 10 for LLM Applications 2025, prompt injection ranks first and for good reason, as direct injection comes from users who are trying to override the model’s instructions. However, indirect injection can be far more sinister for enterprises, as malicious instructions can stay hidden in an email, web page, or document that the model ingests later on during the retrieval process. LLMs cannot always tell instructions apart from the data they’re processing, meaning any poisoned document can direct the model to reveal confidential information or lead the agentic system to send data outside the internal environment.

D. Shadow AI & Unmapped Models

Organizations don’t always have the comprehensive visibility they need into their entire AI infrastructure. Unknown AI agents running in their environments are becoming a fairly prevalent problem, as every unmapped model, plugin, and agent is a potential data flow operating outside both the organization's internal policies and regulatory requirements.

E. Proving Compliance

It’s not enough to be compliant, as both regulators and customers expect organizations to be able to produce documentary evidence corroborating their compliance. Various regulations around the world have begun making such documentation a regulatory requirement, with other obligations such as requiring providers to mark all AI-generated content as such or having strict AI governance policies in place.

The LLMOps Best Practices

The best practices that would cover the operational and security foundations enterprises need to run LLM applications safely and at scale are as follows:

A. Inventory Every Model, Agent, & Data Flow

Regardless of how effective an LLMOps infrastructure is, no organization can govern what it cannot see. Hence, there should be a comprehensive catalog of every LLM use case across the organization, along with all the agents built on top of those models and the connected data sources. This inventory should be treated as a live record that updates automatically and at runtime, rather than a quarterly review.

B. Classify & Sanitize Data Before It Reaches the Model

Every document once made accessible to the model is something it can come back to as part of its fine-tuning or RAG index functions. Hence, it is important to have all sensitive information classified and cataloged as such to ensure all internal and regulatory policies on this matter are enforced as appropriate. This can include masking, redacting, or excluding such data entirely, as it is far cheaper to do so at the source than trying to catch it in model outputs.

C. Enforce Source Permissions at Retrieval

A RAG pipeline must never return content the user was not able to open in the original system. RAG indexing, retrieval, and embedding create various paths for attacks and data leakage that organizations do not fully cover or address.

D. Scope Every Agent & Credential to Its Task

Agents must only be given and retain permissions their tasks require, and only for as long as necessary for their particular tasks. “Excessive agency”, as OWASP describes it, is a common feature in organizations that suffer from AI-related breaches. Hence, organizations must separate staging from production credentials, avoid any shared service accounts, and require human approval for any destructive or irreversible actions.

E. Inspect Prompts, Retrievals, & Responses Inline

Pre-deployment testing cannot anticipate every input a production system will face. Hence, placing runtime controls between users, models, and connected data sources can be highly effective in detecting prompt injection, jailbreak attempts, and sensitive data exposure in both requests and responses.

F. Log Everything

Regulators and auditors require extensive records and proof to show what data a model accessed, what it produced, and who approved this entire workflow. Tamper-resistant logs of all the activities involved, such as prompts, retrievals, outputs, policy decisions, and configuration changes, are extremely effective in such cases. These can then be mapped to frameworks an organization is accountable to, such as the NIST AI Risk Framework or the AI Act. Moreover, ownership can be designed for internal stakeholders across security, data, legal, and engineering teams to make someone accountable for every AI system and its subsequent decisions.

How Securiti Helps

LLMOps is designed to make models faster, more accurate, and affordable. Hence, organizations cannot compromise when it comes to choosing the right tool or platform to leverage these benefits.

Securiti, now a Veeam company, has the DataAI Command Platform, designed to secure all the data moving through the entire AI pipeline. Starting with visibility, it can discover and catalog AI models across public clouds, private clouds, and SaaS applications, including shadow AI. It then maps each model to its data sources, processing, risks, and compliance obligations, and monitors how data flows between them. From there on, teams can discover and classify sensitive data before it reaches fine-tuning sets, RAG pipelines, or third-party AI apps, and enforce controls for data sanitization and access governance.

All models are assessed and classified against global regulatory requirements, helping compliance teams align AI programs with the EU AI Act, NIST AI RMF, and all other major data and AI regulations globally.

Request a demo today and learn more about how the DataAI Command Platform can help your organization secure your LLM pipelines from source data to response.

FAQs About LLMOps

The most commonly asked questions related to LLMOps are as follows: