AI Agents

Let AI agents work — while you stay in control of your data.

Finance copilots, healthcare intake bots, underwriting assistants, grid monitors — AI agents are taking on real enterprise work. But left unchecked, they can overreach, leak sensitive fields, or bypass policies. LLMac acts as a guardrail: every agent query is enforced at runtime, ensuring they operate only within approved boundaries.

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Use Cases

Secure adoption for enterprise AI agents

LLMac ensures autonomous copilots and workflow agents operate strictly within enterprise rules. Every action is filtered at runtime, preventing overreach, sensitive data leaks, or compliance gaps.

Why Enterprises Hesitate with AI

The fear isn’t the LLM. It’s what the LLM can see, remember, and leak.

The top risks enterprises face when connecting AI apps and agents to private data.

Core Enterprise Risks

  • Data leakage — AI pulls unauthorized contracts, HR records, or IP (caused by poor embedding-level control).
  • Prompt injection — Malicious instructions override system intent and expose hidden data or logic.
  • Identity collapse — Shadow agents or shared keys blur runtime boundaries between dev and prod.
  • Agent sprawl — Dozens of autonomous copilots with no clear scopes or audit trails.
  • RAG poisoning — Semantic “nearby” matches retrieve sensitive info outside policy.
  • Compliance breach — GDPR, HIPAA, NDAs broken when data flows to the wrong user or agent.

Why legacy controls fall short

  • IAM/RBAC guard logins — not what an LLM retrieves mid-prompt.
  • API gateways don’t inspect vector payloads or semantic matches.
  • DLP can’t see vectorized or inferred data exposure.
  • OAuth authenticates users — not autonomous AI agents or pipelines.
  • Prompt templates are brittle — no runtime enforcement if inputs drift.
  • Vector DB ACLs are index-level only — no per-query, per-identity, or per-field enforcement.

The cost of inaction

Shadow AI tools Blocked deployments Regulatory fines Re-implementing ACLs everywhere Losing to safer competitors
Trust & Compliance

Audit-first architecture. Zero-trust enforcement.

Policy types

Row-level filtering, field-level masking, boolean/regex rules, time-based windows — all injected at query time.

Audit & Observability

Every query is logged with identity, filters, and latency (tamper-evident JSONL). Export to Splunk, Datadog, or Elastic.

IAM & Governance

Okta, LDAP, SAML, CSV/JWT integration. Designed for least-privilege by default, with roadmap sync to Collibra/Alation.

Privacy & Security

LLMac enforces access at the retrieval layer — privacy is preserved before any model sees context, and every action is provable.

Privacy

  • Data stays in your environment — self-hosted in your VPC or on-prem; nothing leaves your control.
  • Query-time minimization — only ACL-compliant chunks reach the model.
  • Metadata-first indexing — embeddings carry owner/region/dept for precise enforcement.
  • Retention control — configurable TTLs; no silent vendor training.
  • Vendor-agnostic — enforcement applies before OpenAI, Claude, Gemini, or any model call.

Security

  • Granular enforcement — row/field-level ACLs, ABAC/RBAC, and over-fetch prevention.
  • Identity-aware — policies evaluate user & agent context at runtime (Okta, LDAP, SAML supported).
  • Runtime Enforcement SDK — compiles filters directly into vector search queries.
  • End-to-end audit — who, what, when, why logged and exportable to SIEM.
  • Encryption — TLS in transit; AES-256 at rest for data and embeddings.
FAQ

Frequently Asked Questions

Answers to the top questions enterprises ask about LLMac.

How does LLMac integrate with our data sources?
Outcome: Connect once, enforce everywhere. Proof: LLMac integrates with SQL, BigQuery, MongoDB, APIs, and files — extracting keys and metadata so access rules apply consistently without changing your databases.
How is LLMac different from IAM or RBAC?
Outcome: IAM controls logins; LLMac controls retrieval. Proof: We enforce fine-grained access at query time, inspecting embeddings and retrieval calls to prevent semantic leaks.
Can policies go down to row and field level?
Outcome: Only the right data reaches each user/agent. Proof: LLMac policies apply to rows, fields, and vector metadata attributes, filtering before retrieval.
How does LLMac stop prompt injection or RAG poisoning?
Outcome: Prompts can’t bypass enterprise rules. Proof: Every retrieval request is checked against policy, blocking malicious overrides and “nearby” semantic matches that violate permissions.
Where does LLMac run?
Outcome: Data stays in your control. Proof: LLMac is self-hosted in your cloud VPC (AWS, GCP, Azure) or fully on-prem. No SaaS, no external exposure.

Not sure which use case fits your enterprise?

Let’s chat about your data, your teams, and the AI apps or agents you want to deploy. We’ll show you how LLMac adapts to your exact needs.

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