LLM security best practices are hard to enforce across teams because the AI itself is scattered. Models, prompts, agents, MCP servers and SaaS copilots arrive through different teams and tools, each with its own controls, and most enforcement stops at deployment while LLM agents keep making decisions afterward. Guidelines exist (the OWASP Top 10 for LLM Applications and the NIST Generative AI Profile are good starting points), but a document cannot stop a team from shipping an unscanned model or an over-permissioned agent. Enforcement gets easier when every AI artifact passes through one gate, carries its security context with it, and is governed by one policy at runtime.
What are LLM security best practices in the first place?
Most organizations converge on a similar list, largely shaped by OWASP's 2025 risks (prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption).
- Only use approved, scanned models and components.
- Keep sensitive data out of prompts and outputs where it does not belong.
- Give agents the minimum tools and permissions they need.
- Validate and constrain model outputs before they trigger actions.
- Log what AI systems did, so incidents can be investigated.
- Review and re-approve when models, prompts or tools change.
Nobody disagrees with this list. The problem is making it true for every team, every time.
Why do LLM security practices break down between teams?
AI arrives in too many shapes
A traditional application ships as a container image. An LLM application is a model, weights, a system prompt, retrieval data, tool definitions, MCP servers, skill files and configuration, often stored in different places. Security reviews one piece and the others change without review. If there is no single artifact to approve, there is nothing consistent to enforce.
Every team picks its own stack
Data science uses notebooks and Hugging Face. Application teams call hosted APIs. Platform teams run inference on Kubernetes. Business units buy SaaS tools with built-in agents. Each path has different controls (or none), so a policy that is enforced in one path is invisible in the others.
Policies live in documents, not in the path
Many AI policies are PDFs and wiki pages. They depend on each engineer remembering and interpreting them correctly under deadline pressure. Practices that are not automated at the point of use decay quickly, and that is not a discipline problem. It is a design problem.
Enforcement stops at deployment
Even teams with good CI gates usually stop at "approved to deploy." An LLM agent keeps choosing actions at runtime. It can be manipulated by injected instructions in a document or web page, and it can misuse a legitimate tool. OWASP calls the second problem excessive agency. A deploy-time check cannot see either one.
Network controls miss local actions
A common response is to put a gateway in front of model APIs. That helps with traffic, but it does not answer who verified the agent, and it misses actions that never cross the network, such as file operations, shell commands and local (stdio) MCP servers.
Nobody owns the whole chain
Security writes policy, platform engineering owns infrastructure, data science owns models and application teams own agents. When something goes wrong, each team holds part of the evidence. Without one audit trail, investigations become archaeology.
How do you make LLM security enforceable instead of aspirational?
- Make every AI component an artifact. Package models, prompts, datasets, agent definitions and configuration as one versioned unit. KitOps ModelKits do this with OCI artifacts, so the same registries and CI/CD tools that handle containers can handle AI.
- Create one intake path. Bring external models and components into a private registry rather than letting each team pull from the internet. Scan on intake and on every new version, and keep the results attached to the artifact.
- Write policy once, enforce it in several places. The same rule should apply when an artifact is pulled, when it is admitted to a cluster and when an agent tries to act. If each stage has its own rules, the gaps between them become the attack surface.
- Govern behavior at runtime. Evaluate agent actions against policy as they happen. Block dangerous operations, redact sensitive data and isolate agents from what they should not touch.
- Keep one audit trail. Record what was deployed, who approved it, and what each agent was allowed, blocked or redacted from doing. Send it to the SIEM your security team already watches.
- Make the secure path the easy path. Data scientists and developers should keep their tools. Controls should run in the background of the registry, pipeline and runtime, not as extra forms.
Where does Jozu fit?
Jozu is built around those six steps for AI running on Kubernetes. Jozu Hub is the private intake registry where ModelKits are scanned and approved, with results that travel with the artifact. Agent Guard admits only approved artifacts and keeps enforcing policy while agents run, through its Policy Gateway and Agent Enclave, which isolates each agent in its own microVM. Every decision lands in one audit log. Jozu is also extending the same runtime policy to AI running on endpoint devices, so desktop and SaaS AI can follow the same rules as home-grown agents.
Other tools address parts of this problem. Agentic security vendors focus on discovering and controlling agents. Artifact platforms such as JFrog Artifactory and Sonatype Repository Firewall have added Hugging Face model scanning. Large security vendors are adding AI features to their suites. The useful question for any of them is whether one policy reaches every stage, from intake to runtime.
FAQ
Why aren't AI usage policies enough on their own? Written policies rely on people applying them consistently. Without automated gates in the registry, pipeline and runtime, they erode as teams move fast.
What is the biggest LLM security gap in most organizations? Commonly, the lack of control after deployment. Agents act at runtime, and a deploy-time approval cannot see prompt injection or misuse of legitimate tools.
Which frameworks define LLM security best practices? The OWASP Top 10 for LLM Applications and NIST AI 600-1, the Generative AI Profile of the NIST AI Risk Management Framework, are widely used references.
Is an AI gateway enough to enforce LLM security? A gateway helps with network traffic, but it cannot verify where an agent came from and misses local actions such as file operations, shell commands and stdio MCP servers.
How do you get data science teams to follow security practices? Put controls in the tools they already use. If models flow through a registry that scans and approves them automatically, compliance becomes the default rather than an extra step.
CTA: See how one policy follows your AI from intake to runtime. [Book a demo]