Secure Customer and Internal AI Interactions

RAG boosts accuracy but expands the attack surface across model and retrieval layers. Pebblo unifies both with Safe Infer and Safe RAG so you can scale chatbots with confidence.

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Why Traditional Controls Fall Short

On the model side, interactions risk leaking sensitive data and often lack fine-grained model selection. On the retrieval side, chat can expose internal content, violate compliance, or be hijacked by data-borne command-injection attacks. Recent events (e.g., EchoLeak) show how hidden instructions in data can be abused to extract sensitive information, underscoring the need for integrated defenses.

Sensitive data leakage to model providers

Over-exposure from retrieval across mixed-sensitivity sources

Data-borne injection and social-engineering attacks

Pebblo’s answer

A dual-layer architecture: Safe Infer governs model interactions (routing, filtering, validation), and Safe RAG secures retrieval (safe connectors, auth-aware access, injection defense). A single policy plane keeps them in lockstep.

Safe Infer

Policy-based model routing, sensitive-data filtering before the model, and response validation prior to display.

Safe RAG

Safe connectors with fine-grained permissions for Slack, SharePoint, Salesforce, and more; authorization-aware retrieval to your vector DB.

Injection Defense

Stops command-injection attacks hidden in data by stripping malicious instructions at retrieval, before the content reaches the model.

Safe Controls

Built-in enforcement for HIPAA, GDPR/CCPA, and data residency with per-group model policies.

How it works

Safe Infer — Model Layer

Route by user/group to approved models; redact secrets and sensitive fields; validate completions for appropriateness and compliance.

Safe RAG —
Retrieval Layer

Govern retrievers and connectors with auth-aware queries, secure VectorDB, and layered defenses against data-borne injections.

Unified Policy &
Observability

One policy plane covers model + retrieval with full audit trails, alerts, and dashboards for security and CX teams.

How it works

Safe Infer -
Model Layer

Route by user or group to approved models; redact secrets and sensitive fields; validate completions for appropriateness and compliance.

Safe RAG -
Retrieval Layer

Govern retrievers and connectors with auth-aware queries, a secure vector DB, and layered defenses against data-borne injections.

Unified Policy &
Observability

One policy plane covers model and retrieval with full audit trails, alerts, and dashboards for security and CX teams.

// Benefits

Production-ready chatbot security

Developer-friendly

Update LLM URL and retriever URL (auto-provisionable); no major rewrites; SDK optional.

Human oversight

Via configurable approvals and guardrails, without blocking automation.

Keep your stack

LangChain, LlamaIndex, Haystack, custom RAG, and bring-your-own components.

Data identity

Authorization on every fetch enforces least-privilege access.

Automated compliance

For GDPR, CCPA, and HIPAA with per-use-case model routing.

Reasoning-driven retrieval

For GDPR, CCPA, and HIPAA: preserves context while protecting sensitive data, with per-use-case model routing.

BYO RAG Pipeline

Plug in your own retriever, chunker, and vector DB while Pebblo enforces policy.

// Proven outcomes

Financial Services -Banking Agents (LangGraph)

Watch langGraph Banking Agents Demo

Banking agents (LangGraph) analyze market and internal data through MCP connections. Policies restrict access to insider and client data, prevent barrier breaches, and enable compliant automation.

Prevented information-barrier violations

Research velocity maintained

End-to-end auditability

Healthcare -Automation Agents (CrewAI)

Watch CrewAI Healthcare Agents Demo

CrewAI healthcare agents coordinate claims, care, and operational tasks across multiple backends. Pebblo enforces PHI/PII protection and regulatory alignment without limiting automation gains.

PII safeguards

Regulatory compliance upheld

Higher straight-through processing

// FAQ’s

We’re here to answer your questions

View Datasheet

Does this slow responses?

No. Safe Infer and Safe RAG add only micro-latency with policy caching and streaming, preserving UX.

Can we keep our current VectorDB?

Yes. We secure connections to your existing stores (e.g., pgvector, Pinecone, Milvus, Elastic, or proprietary KBs).

Do we need an SDK?

No. Integration works by updating the LLM URL and retriever URL; SDKs are optional.

How do you stop EchoLeak-style attacks?

We filter and validate at retrieval and generation time, removing malicious instructions found in retrieved chunks and model outputs before they reach the model.

Secure Customer and Internal AI Interactions

Why Traditional Controls Fall Short

Pebblo’s answer

Safe Infer

Safe RAG

Injection Defense

Safe Controls

How it works

Safe Infer — Model Layer

Safe RAG —
Retrieval Layer

Unified Policy &
Observability

How it works

Safe Infer

Safe Agent

Policy Plane & Visibility

How it works