> ## Documentation Index
> Fetch the complete documentation index at: https://docs.beyondguard.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# The AI Security Coverage Gap and How BeyondGuard Closes It
> Learn how BeyondGuard closes the AI security coverage gap with purpose-built guards that protect every node of your AI value chain.
Traditional security tools were not designed to reason about AI behavior. A firewall inspects packets. A DLP system pattern-matches on known data signatures. A CASB governs which cloud services employees can access. None of these tools can interpret a crafted prompt, evaluate whether an agent's execution plan has deviated from its intended scope, assess the safety of a model's generated output, or detect poisoned context in a RAG retrieval pipeline. When you deploy LLMs, agentic AI, or retrieval-augmented systems, you introduce a threat surface that your existing security stack cannot see — let alone protect.
## The Coverage Gap
AI systems interact with the world in ways that no prior generation of enterprise software did. A single user message can instruct a model to ignore its system prompt, exfiltrate data through a tool call, or manipulate a downstream agent. These threats are not theoretical: they are actively exploited in production systems across banking, healthcare, legal, and government sectors.
The coverage gap has several dimensions:
* **Prompt-layer threats** — Jailbreaks, prompt injection attacks, and system prompt leakage occur before a model even begins generating a response. Traditional input validation cannot detect semantically constructed attacks.
* **Agent-layer threats** — Autonomous agents make multi-step plans and execute tool calls. Scope violations, plan deviation, and memory poisoning are risks that emerge only at runtime, across sequences of actions.
* **MCP and tool-layer threats** — Model Context Protocol integrations and external tool calls expose parameters, schemas, and APIs to untrusted input. Tool poisoning and parameter injection can redirect agent behavior without triggering any application-level alert.
* **Data-layer threats** — RAG pipelines retrieve external content and inject it into model context. Poisoned documents and PII-bearing embeddings can silently corrupt model responses.
* **Output-layer threats** — Models can generate toxic content, leak confidential data, or produce outputs that violate regulatory requirements. No model is immune, regardless of fine-tuning or system prompt hardening.
Existing security tools sit outside this value chain and cannot intervene at these points. BeyondGuard was built to close this gap.
## How BeyondGuard Works
BeyondGuard positions itself as an independent security layer between your users and your AI models. It does not modify model weights, alter inference behavior, or require you to change your hosting infrastructure. Instead, it intercepts interactions at each layer of the AI stack, evaluates them against your security policies, and acts — blocking, flagging, redacting, or alerting — before harm can propagate.
Six layers of the AI stack are protected:
| Layer | What It Represents |
| --------------- | ----------------------------------------------------------- |
| **Data** | Embeddings, vector stores, and retrieval pipelines |
| **Model** | The LLM or foundation model itself |
| **Application** | The application logic wrapping the model |
| **Agent** | Autonomous agents executing multi-step plans |
| **MCP / Tool** | External tool calls and Model Context Protocol integrations |
| **User** | Output delivery to end users |
**Structural independence** is a foundational design principle. Because BeyondGuard makes security decisions separately from the models it protects, it cannot be manipulated by the same inputs that target those models. A jailbreak that bypasses an LLM's safety training does not bypass BeyondGuard, because BeyondGuard's evaluation logic runs outside the model's inference context entirely.
All guards report to a single **Control Plane**, giving security and engineering teams a unified view of threats, policies, and compliance posture across every AI project in the organization.
## Key Capabilities
Detects and blocks jailbreaks, prompt injection, and system prompt leakage before they reach your model.
Monitors autonomous agent plans for scope violations, unexpected tool usage, and mid-execution deviation.
Validates tool calls and MCP integrations against expected schemas and parameter boundaries to prevent tool poisoning and parameter injection.
Inspects RAG retrieval pipelines for poisoned documents, PII-bearing embeddings, and unsafe context injection.
Reviews model-generated responses for toxic content, data leakage, and regulatory violations before delivery to users.
## Who Uses BeyondGuard
BeyondGuard is built for enterprises deploying LLMs, agentic AI, and RAG systems in industries where the cost of a security failure is high. This includes financial services institutions running AI-assisted underwriting or customer service, healthcare organizations using LLMs for clinical decision support, legal and compliance teams deploying document analysis agents, and government agencies building internal AI tools on sensitive data.
BeyondGuard supports compliance with the **OWASP LLM Top 10** and the **EU AI Act**, and its maturity model — progressing from L0 (Unmanaged) through L5 (Continuous Governance) — gives organizations a structured path to meet both internal security standards and external regulatory requirements. Organizations in demanding sectors such as banking have used BeyondGuard to move from no AI-specific controls to active governance without disrupting existing model deployments.
Ready to see BeyondGuard in action? Head to the [Quickstart](quickstart) to connect your first AI pipeline and deploy a guard in minutes.