Securely harness Retrieval‑Augmented Generation (RAG)
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RAG is transformative and risky if left ungoverned
RAG pipelines ingest external and internal content by design. If an attacker injects malicious or false data into a vector store, the AI will treat it as gospel, leading to misinformation or policy violations.
Knowledge bases often contain PII/PHI and proprietary IP. Without guardrails, RAG systems can retrieve and surface confidential data, violating privacy laws and eroding customer trust.
Attackers can hide instructions in retrieved text to coerce a model into leaking secrets or performing unauthorized actions, bypassing traditional network controls.
Teams spin up separate RAG experiments using different tools. This fragmentation duplicates effort and leaves security gaps because each pipeline handles data and access differently.
Building RAG involves vector databases, embedding models and orchestration across agents and LLMs. Many enterprises lack the expertise to deploy and secure these components at scale.
Conventional security products (WAFs, DLP, IAM) don’t understand prompts, embeddings or agent chains. Without RAG‑aware monitoring and enforcement, organizations fear they won’t detect or stop novel attacks.
End‑to‑end RAG protection and control – from ingestion to generation
Sensitive‑Data Monitoring & Vector‑Store Governance
Levo identifies and labels sensitive data flowing into vector stores and RAG pipelines, tracking upserts, queries and corpus namespaces. By tying each retrieval to the requesting agent or session, enterprises can prevent unauthorized access and build an immutable audit trail.
Inline Data‑Flow Protection & Guardrails
Runtime policies block or redact PHI, PII, secrets and IP before they reach external models, RAG/vector stores or end‑user responses. Rules can allow, deny or rewrite prompts, outputs and embeddings based on identity, data class, vendor or region, ensuring that even complex agent chains stay within compliance.
Hallucination & Integrity Testing
Continuous hallucination robustness tests validate that model outputs reference only authorized data, catching when the AI tries to cite unsupported or unauthorized sources. This keeps answers trustworthy and protects against subtle data‑leakage attacks.
Unified Visibility & Policy Enforcement
Levo maps every AI component: agents, LLMs, MCP servers, APIs and vector stores, creating a real‑time graph of who accessed what and when. Policies codified as code can enforce role‑based access, geo restrictions and least‑privilege retrieval without developer slowdown.
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RAG Security that supports both: Enterprise AI and Enterprise Teams
Launch RAG‑powered AI features faster without building custom security. Levo handles RAG security so your developers can focus on innovation rather than plumbing and patching.
Gain continuous visibility into vector stores, embeddings and retrieval operations. Stop worrying about unsanctioned RAG pilots; enforce consistent guardrails across all teams.
& Risk Officers
Prove that RAG usage respects privacy, region and industry‑specific regulations. Adopt RAG with confidence that your AI is compliant.
Retrieval shouldn’t mean risk. Secure your AI pipelines with Levo
Frequently Asked Questions
Got questions? Go through the FAQs or get in touch with our team!
How does RAG work?
A Retrieval‑Augmented Generation (RAG) system stores documents or data as vectors (numerical representations) in a vector database. When a query arrives, it retrieves the most relevant context from that store, feeds it into the model, and generates an answer that blends stored knowledge with the model’s training.
Why are enterprises adopting?
RAG enables AI to answer questions with the latest company knowledge (policies, product specs, recent reports) without retraining models. It improves accuracy, provides citations, and scales across customer service, legal, finance and healthcare.
What are the risks associated with RAG?
Because RAG pipelines ingest and retrieve data by design, they can inadvertently leak sensitive information or ingest malicious content. Poisoned datasets can lead to misinformation, while prompt‑injection attacks can cause the model to perform unsafe actions.
How can I secure RAG pipelines?
Security for RAG includes monitoring what data flows into and out of vector stores, controlling access to embeddings, enforcing data masking/redaction on sensitive fields, and validating that outputs reference only authorized sources. This requires tools built specifically for RAG.
What is Levo’s role in RAG Security?
Levo provides runtime monitoring, governance, threat detection and protection that are designed for RAG. Its platform monitors vector stores and RAG pipelines to label sensitive data, blocks or redacts PHI/PII/secret data before it enters or leaves the system and continuously tests for hallucinations or poisoning in outputs.
How does Levo defend against prompt-injection attacks and jailbreaks?
Levo’s inline guardrails enforce policies across prompts, outputs and embeddings. They score inputs for injection patterns and can rewrite, block or sandbox requests. This prevents malicious instructions from propagating through the RAG pipeline.
Does Levo help with compliance and audits?
Yes. Levo logs who accessed data, what vector queries were made and how sensitive data was handled, creating immutable audit trails. Policies (e.g., “No PHI to non‑US models”) are enforced automatically at runtime, helping enterprises meet GDPR, HIPAA and other regulatory requirements.
What other Levo features complement RAG security?
Beyond RAG Security, Levo extends beyond visibility to cover the full spectrum of AI security. Its breadth spans MCP servers, LLM applications, AI agents, and APIs, while its depth runs from shift-left capabilities like discovery and security testing to runtime functions such as monitoring, detection, and protection. By unifying these layers, Levo enables enterprises to scale AI safely, remain compliant, and deliver business value without delay.
How does Levo compare to traditional WAFs or DLP tools?
Traditional security tools don’t parse prompts or embedding queries. Levo is built to understand AI interactions; it inspects east‑west traffic between agents, vector stores and MCP servers, flags anomalies, and enforces policies without introducing latency.
How is sensitive data protected?
Gateways and firewalls see prompts and outputs at the edge. Levo sees the runtime mesh inside the enterprise, including agent to agent, agent to MCP, and MCP to API chains where real risk lives.
How is this different from model firewalls or gateways?
Live health and cost views by model and agent, latency and error rates, spend tracking, and detections for loops, retries, and runaway tasks to prevent outages and control costs.
What operational insights do we get?
Live health and cost views by model and agent, latency and error rates, spend tracking, and detections for loops, retries, and runaway tasks to prevent outages and control costs.
Does Levo find shadow AI?
Yes. Levo surfaces unsanctioned agents, LLM calls, and third-party AI services, making blind adoption impossible to miss.
Which environments are supported?
Levo covers LLMs, MCP servers, agents, AI apps, and LLM apps across hybrid and multi cloud footprints.
What is Capability and Destination Mapping?
Levo catalogs agent tools, exposed schemas, and data destinations, translating opaque agent behavior into governable workflows and early warnings for risky data paths.
How does this help each team?
Engineering ships without added toil, Security replaces blind spots with full runtime traces and policy enforcement points, Compliance gets continuous evidence that controls work in production.
How does Runtime AI Visibility relate to the rest of Levo?
Visibility is the foundation. You can add AI Monitoring and Governance, AI Threat Detection, AI Attack Protection, and AI Red Teaming to enforce policies and continuously test with runtime truth.
Will this integrate with our existing stack?
Yes. Levo is designed to complement existing IAM, SIEM, data security, and cloud tooling, filling the runtime gaps those tools cannot see.
What problems does this prevent in practice?
Prompt and tool injection, over permissioned agents, PHI or PII leaks in prompts and embeddings, region or vendor violations, and cascades from unsafe chained actions.
How does this unlock faster AI adoption?
Levo provides the visibility, attribution, and audit grade evidence boards and regulators require, so CISOs can green light production and the business can scale AI with confidence.
What is the core value in one line?
Unlock AI ROI with rapid, secure rollouts in production, powered by runtime visibility across your entire AI control plane.
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