Agies
Runs in your environment · SaaS or self-hosted

AI security that never leaves your network.

Agies inspects every prompt, model, agent, and connector inside your own environment — the breadth is the proof. Run it as managed SaaS, where only the security signals you choose are shared, or fully self-hosted in your own Kubernetes, where nothing leaves at all.

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  • Data never leaves your network
  • Enforced in your environment
  • SaaS or self-hosted (EKS)
Agies topology Inside your environment, an Agies connector inspects traffic from your apps, browser, agents, models, and cloud, and proxies to providers like OpenAI and Anthropic using keys held in your own vault. Only security events and findings — which you control — cross the boundary to the Agies cloud control plane, where the dashboard, policy, and Threat Center run. YOUR ENVIRONMENT Apps Browser Agents Models Cloud Agies connector runs in your env gateway · firewall scan · discover agent guard keys ← your vault inspect locally OpenAI Anthropic keys stay local signals Agies cloud control plane dashboard · policy · RBAC Threat Center · SIEM Only the security signals you choose leave your environment.
Full-spectrum AI security

One platform across the whole AI security lifecycle — in your environment.

Discover the AI you're running, govern how it's used, defend it in real time, test it like an attacker, and prove it to auditors — with enforcement inside your own network, not a vendor's cloud.

  1. 01 Discover
  2. 02 Govern
  3. 03 Defend
  4. 04 Test
  5. 05 Prove

Stage 01 · Discover

Find the AI you didn't know you were running

You can't secure what you can't see. Agies shows you every AI tool, agent, and model in your organization — who's using it, on which device, and whether it was ever sanctioned.

Under the hood

  • Shadow-AI discovery. Inventories AI tools, agents, and MCP servers across your machines, tied to a person and a device, and flags unsanctioned MCP servers — discovered, not self-reported.
  • AI-BOM (supply chain). Emits a CycloneDX 1.5 AI bill of materials with model components, SHA-256 hashes, licenses, and base-model provenance.

Stage 02 · Govern

Put AI usage under governance and control

Give every team access to AI without handing out raw provider keys or losing control of spend. Set the rules in plain English; Agies turns them into enforced policy and keeps your real keys in your own vault.

Under the hood

  • Virtual-key AI gateway. OpenAI/Anthropic-compatible proxy with scoped per-key budgets, rate limits, and model allow-lists — the OWASP LLM10 control. Real provider keys stay in your vault.
  • Policy Studio. Describe intent in plain English; it compiles into validated firewall, agent-guard, and MCP rules — grounded to controls that actually exist, with one-click revert.
  • Role-based access control (RBAC). Admin, analyst, and viewer roles over the whole control plane, with every action written to an append-only, hash-chained audit log.

Stage 03 · Defend

Stop attacks and leaks in real time

Block prompt-injection and jailbreaks, redact PII and secrets before they leave, and stop agents and their tools from doing something they shouldn't — inline, as it happens, inside your network.

Under the hood

  • 13-detector LLM firewall. DistilBERT-based inspection of prompts and responses — prompt-injection protection with evasion/obfuscation defeat, PII redaction, secrets and exfiltration — on an allow / flag / redact / block matrix. The same inspection covers the retrieval channel that feeds RAG.
  • MCP security & tool-call validation. Sanction which servers and tools an agent may reach; inspect every tool call — arguments and results — mapped to OWASP LLM01, with full session replay.
  • Cryptographic agent identity. Real agent identities with an append-only, hash-chained audit log and an inline guard on every action.
  • Browser guard & model scanning. Extends the firewall to ChatGPT and Claude in the browser, and scans models in place without executing them — a malicious pickle scores critical before it can load.

Stage 04 · Test

Attack your own AI before someone else does

Find the weaknesses in your models and agents on your terms. Agies runs adversarial campaigns and hands you a scorecard you can act on — and take to your board.

Under the hood

  • AI red-teaming. Adversarial attack packs — jailbreak, data-exfiltration, encoding-evasion, excessive-agency, indirect-injection, memory/RAG-poisoning and more — mapped to the OWASP LLM Top 10 and OWASP Agentic risks.
  • Scorecards. Every campaign returns pass-rates and severity-weighted findings, so you can prove a fix actually landed.

Stage 05 · Prove

Show auditors and leadership the evidence

Turn everything Agies enforces into audit-ready evidence, and pipe it into the security tools your team already lives in — no manual screenshotting, no asserted badges.

Under the hood

  • Evidence-linked AI compliance. Coverage computed from live signal across OWASP LLM Top 10, OWASP Agentic, NIST AI RMF, and EU AI Act — each control linked to a real rule, red-team run, or record. Transparency, not a badge we assert.
  • SIEM / SOAR export. Native export to Splunk, Microsoft Sentinel, PagerDuty, Jira, ServiceNow, syslog, and webhook — in CEF/ECS.
The problem

AI adoption has outrun AI security.

Developers wire apps to OpenAI/Anthropic with raw provider keys — no limits, no spend control, no record of what was sent.

Employees paste source code, PII, and credentials into ChatGPT and Claude in the browser — traffic no corporate control ever sees.

Teams run models pulled from Hugging Face — pickle files that can execute arbitrary code the moment they load.

Autonomous agents take real actions with no identity, no audit trail, and no guardrails.

Nobody has an inventory of the AI running in the environment, or can prove compliance when asked.

The platform

Connectors in your environment, one control plane in the cloud.

Lightweight connectors deploy inside your environment and inspect every AI surface in real time — locally, so prompts, responses, keys, and model weights never leave your network. Each connector reports the security signals you approve to the Agies cloud control plane, where a single correlation engine opens incidents. You manage the whole fleet — policy, keys, findings — from one place.

  1. Connect

    Deploy a connector into each environment. It mTLS-enrolls to your workspace, outbound-only — no inbound ports.

  2. Enforce locally

    Block prompt injection, redact PII and secrets, cap spend, and deny over-privileged agent actions — inline, in your env.

  3. Scan & discover

    Scan models in place before they ship and discover the AI already running — findings surface centrally, artifacts stay local.

  4. Correlate in the cloud

    The signals you send fold into ranked incidents, alert your team, and generate the compliance evidence.

Deployment

Two ways to run it — both keep your data yours.

Managed SaaS

Connectors deploy in your environment (outbound-only, mTLS, no inbound ports). Your AI traffic is inspected locally; only the security signals you choose reach the managed control plane. Fastest way to start.

Self-hosted (EKS)

Run the entire platform — control plane included — in your own Kubernetes cluster. Nothing leaves your environment. Built for teams that can't send data off-site.

The platform at a glance

The proof we can enforce it everywhere:

Ten pillars, one correlation brain.

Virtual Key Gateway

A drop-in OpenAI/Anthropic-compatible proxy — scoped virtual keys per team with budgets, rate limits, model allow-lists, and full logging, while the real key stays in your own vault (HashiCorp Vault or Agies's AES-GCM store) and never reaches the cloud.

LLM Firewall

Policy-driven inspection of prompts and responses — 13 detectors spanning prompt-injection (with evasion/obfuscation defeat), jailbreak, PII, secrets, and exfiltration, on a tunable allow / flag / redact / block matrix. The same inspection covers the retrieval channel that feeds RAG.

Browser Guard

A Chrome/Edge extension that extends the firewall to ChatGPT and Claude in the browser — text only ever goes to your in-environment connector, never to the cloud.

Model & Artifact Scanning

Scans models in your environment without ever executing them — flags a malicious pickle as critical while passing a clean model, and emits a CycloneDX AI-BOM with component hashes, licenses, and base-model provenance. Weights stay local; only the findings surface centrally.

AI Red-Teaming

Run adversarial campaigns against your models and agents — jailbreak, data-exfiltration, encoding-evasion, excessive-agency, indirect-injection, memory/RAG-poisoning, and more, in attack packs mapped to the OWASP LLM Top 10 and OWASP Agentic risks. Every campaign returns a scorecard with pass-rates and severity-weighted findings.

AI Discovery & Visibility

Finds the AI running in your environment — local model servers, agents, and MCP servers — and ties each one to the person and device using it. Discovery is live and continuous, so shadow AI shows up before it becomes an incident.

Agent Identity & Monitoring

Gives agents real identities and an append-only, hash-chained audit log, with an inline guard on tool calls.

AI Usage Visibility

Inventories every AI tool, agent, and MCP server across your machines and ties each one to a person — pick a user, see their devices and what they've connected. Metadata-first: it records that a credential exists, never its value.

MCP Security

Sanction which MCP servers and tools an agent may reach, and inspect every tool call — arguments and results — so exfiltration and injection arriving back from a tool get caught. Full audit and session replay.

Policy Studio

Describe a policy in plain English; Agies compiles it into validated rules across the firewall, agent guard, and MCP. It can only emit controls that actually exist — no invented rules — and every change reverts in one click.

Platform detail

Every control, named — no summaries.

The five stages above, expanded into the full technical surface Agies enforces — in precise terms, for the security team that has to approve it. Everything listed is live today.

Show the full control list 19 controls across the platform

Cryptographic agentic registration

Cryptographic agent identity with a self-enrollment SDK; every agent action is written to an append-only, hash-chained (tamper-evident) audit log.

Virtual-key LLM gateway

OpenAI/Anthropic-compatible proxy with per-key budgets, rate limits, and model allow-lists — the OWASP LLM10 control. Real provider keys stay in your vault.

Inline ML firewall — 13 detectors

DistilBERT-based inline inspection of prompts and responses across 13 detectors and 400+ threat signatures, on an allow / flag / redact / block matrix.

Prompt-injection detection

Fused heuristic + ML detection that defeats evasion and obfuscation on multi-layer and multi-turn injection attempts.

PII detection & redaction

Fused ML + rules pipeline that redacts SSNs, emails, secrets, and financial/health data inline, before anything leaves your environment.

MCP security

Sanction which MCP servers and tools an agent may reach; inspect every tool call — arguments and results — with full session replay.

Tool-call validation

Argument-level inspection and blocking of agent tool calls (jailbreak, prompt-injection), mapped to OWASP LLM01, with per-call latency recorded.

Model & artifact scanning

Scans models in place without ever executing them — catches malicious pickles and unsafe artifacts before they load.

Model risk scoring

Per-artifact verdict and severity for every scanned model — a clean model passes, a weaponized pickle scores critical.

AI supply-chain security (AI-BOM)

Emits a CycloneDX 1.5 AI-BOM with model components, SHA-256 hashes, licenses, and base-model provenance.

AI red-teaming

Adversarial campaigns with scorecards, pass-rates, and severity-weighted findings — attack packs mapped to the OWASP LLM Top 10 & OWASP Agentic risks.

NL Policy Studio

Plain-English intent compiled into validated firewall, agent-guard, and MCP rules — grounded to real controls, with one-click revert.

Shadow-AI discovery

Discovers the AI tools, agents, and MCP servers running in your environment, tied to a person and a device — and flags unsanctioned MCP servers.

SIEM / SOAR export

Native export to Splunk, Microsoft Sentinel, PagerDuty, Jira, ServiceNow, syslog, and webhook — in CEF/ECS.

Sensor & agent fleet

Register and heartbeat in-environment sensors and enrolled agents, managed from one control plane.

Cross-platform coverage

One control plane, everywhere your AI runs — macOS, Linux, Windows, and Kubernetes.

Compliance transparency

Evidence-linked coverage across OWASP LLM Top 10, OWASP Agentic, NIST AI RMF, and EU AI Act reporting — computed from live signal, not a badge we assert.

Data sovereignty & deployment

Managed SaaS (only the security signals you choose leave) or self-hosted EKS (nothing leaves your environment at all).

Role-based access control (RBAC)

Admin, analyst, and viewer roles across the control plane, with every action written to the append-only, hash-chained audit log.

Scope note: Agies runs as single-org / per-customer deployments today — the depth above is validated on real software and hardware.

New in the platform

The agentic surface — seen, sanctioned, and governed.

Three additions that extend the same wedge: AI-usage telemetry and MCP traffic are inspected inside your environment, exactly like prompts, keys, and model weights already are.

AI Usage Visibility & Identity

Shadow AI isn't a policy problem until you can name it. A lightweight endpoint agent and browser extension inventory AI usage across your machines — so “who is using Claude or ChatGPT, doing what, on which device” finally has an answer. One control plane, everywhere your AI runs — macOS, Linux, Windows, and Kubernetes.

  • Inventory the whole estate. Every AI tool, agent, and MCP server running across your org, discovered rather than self-reported.
  • Pivot by person. Pick a user and map their machines, their agents, and the MCP servers they've connected.
  • Presence-only for credentials. Records that an API key exists and where it lives — never captures the secret value.

Where it runs. The endpoint agent and extension report to your connector. The inventory is built in your environment; only the usage signals you choose reach the control plane.

MCP Security

MCP turns an agent's tools into a live network boundary — one your existing controls can't see. Agies sanctions which servers and tools an agent may use, then inspects every call end to end.

  • Sanction the surface. Allowlist exactly which MCP servers and tools each agent is permitted to reach.
  • Inspect arguments and results. Catches data exfiltration on the way out and indirect prompt injection arriving back from a tool.
  • Audit and replay. Every tool call recorded, with full session replay for the incident review.

Nothing new to install. Remote MCP is inspected inline by the connector you already deployed; local MCP is covered by the endpoint agent. No extra appliance, no new hop.

Policy Studio

Write a security policy in plain English and Agies compiles it into enforced rules. Describe the intent; the platform turns it into validated controls across the firewall, agent guard, and MCP.

  • Intent in, rules out. One description becomes concrete, validated rules spanning every enforcement point.
  • No hallucinated rules. The compiler can only emit controls that actually exist — a policy either maps to real enforcement or it doesn't compile.
  • One-click revert. Every compiled change is reversible, and the audit log records who changed what.

Grounded by construction. Policy Studio is bounded by the control set Agies implements — so plain English buys you speed, not a rule that silently enforces nothing.

Why it's different

Built for a security team that has to approve it.

Every marquee capability is validated against real software and hardware — not just tested in a lab. Proven on real Windows, macOS, Linux, and Kubernetes — including a real MCP server and a live cluster.

01

Enforcement in your environment

Connectors run inside your network and inspect traffic locally. Prompts, responses, provider keys, and model weights never leave — you choose which security signals reach the cloud. That's the difference between a tool your security team can approve and one they can't.

02

One control plane, not ten point tools

Gateway, firewall, browser, models, agents, and discovery all feed a single cloud correlation brain — so a leaked secret in a browser chat and a risky agent action show up in the same incident queue.

03

Covers the surfaces others miss

Browser AI usage, shadow-AI sprawl, MCP tool calls, and model-supply-chain attacks (malicious pickles) are the exposures that actually bite — and the ones API-only gateways ignore.

Standards & trust

Compliance you can audit, not a badge we assert.

Agies computes coverage from your live signal and links every satisfied control to real evidence — the actual rule, benchmark run, incident, or agent token. A fresh deploy shows a realistic, honest number and tells you exactly what to configure to close the gaps across OWASP LLM Top 10, OWASP Agentic, NIST AI RMF, and the EU AI Act — coverage you can audit, never a badge we assert.

Mapped to the OWASP LLM Top 10 and OWASP Agentic risks, with audit-ready compliance reporting and native SIEM/SOAR export — Splunk, Microsoft Sentinel, PagerDuty, Jira, ServiceNow, syslog, and webhook, in CEF/ECS.

OWASP LLM Top 10 OWASP Agentic Audit-ready reporting SIEM export (CEF/ECS) NIST AI RMF & EU AI Act reporting
Where it stands. Live today: the virtual-key gateway, the inline ML firewall, model & artifact scanning, AI red-teaming, Threat Center correlation, AI discovery & visibility, MCP and agent security, role-based access control, and Policy Studio — validated on real software and hardware across macOS, Linux, Windows, and Kubernetes. And you decide what data ever leaves your environment.

Deployment & trust

  • Enforcement in your environment. Connectors deploy as outbound-only containers, mTLS-enrolled to your workspace — no inbound ports, and your AI traffic never transits our cloud.
  • Bring your own vault. Agies reads provider keys from your own HashiCorp Vault, or its own AES-GCM store — your keys are never copied to the Agies cloud.
  • Identity, access & audit. Role-based access control — admin, analyst, and viewer roles — with append-only, hash-chained audit logging of every action.
  • Integrations. OpenAI- and Anthropic-compatible API surface, native SIEM/SOAR integration — Splunk, Microsoft Sentinel, PagerDuty, Jira, ServiceNow, syslog, and webhook (CEF/ECS) — and CycloneDX AI-BOM export.
See it in action

Watch Agies secure a live AI stack.

A short walkthrough of the full lifecycle — discover, govern, defend, test, and prove — running against a real environment. Prefer it live? We'll tailor a demo to your own stack.

Questions, answered

AI security FAQ.

The questions security and platform teams ask before they deploy AI at scale.

Does my data leave my network?
No — enforcement runs on connectors inside your own environment, so prompts, responses, provider keys, and model weights are inspected locally and never transit the Agies cloud. In managed SaaS, only the security signals you choose are shared with the control plane; self-hosted, nothing leaves your network at all.
What does Agies protect?
Every way your organization uses AI: the LLM APIs your apps call (through a virtual-key gateway), the models you ship (artifact scanning and AI-BOM), the autonomous agents you run (cryptographic agent identity, tool-call validation, and MCP security), and the chatbots employees paste into in the browser — all inspected by a 13-detector LLM firewall with prompt-injection protection and PII redaction.
Can I run Agies as SaaS or self-hosted?
Both. Managed SaaS deploys outbound-only connectors in your environment and runs the control plane for you, sharing only the signals you approve. Self-hosted runs the entire platform — control plane included — in your own Kubernetes cluster, so nothing leaves your environment. Both keep your data yours.
Which compliance frameworks does Agies map to?
Agies computes evidence-linked coverage against the OWASP LLM Top 10, OWASP Agentic risks, NIST AI RMF, and the EU AI Act, tying each satisfied control to a real rule, red-team run, or audit record. It is computed coverage with evidence — not an asserted badge or an accredited certification.
How does Agies stop prompt injection?
The LLM firewall fuses heuristic detection with a fine-tuned DistilBERT model to catch prompt-injection and jailbreak attempts — including multi-layer and multi-turn obfuscation and evasion — inspecting both prompts and responses inline and blocking or redacting before anything reaches the model or the user.
Does Agies find shadow AI and unsanctioned tools?
Yes. Shadow-AI discovery inventories the AI tools, agents, and MCP servers running across your machines, ties each to a person and a device, and flags unsanctioned MCP servers — so AI usage is discovered rather than self-reported.
Does Agies secure AI agents and MCP tool calls?
Yes. Agents get a cryptographic identity and an append-only, hash-chained audit log, with an inline guard on their actions. For MCP, Agies sanctions which servers and tools an agent may reach and inspects every tool call — arguments and results — to catch data exfiltration on the way out and indirect prompt injection arriving back from a tool.
Get a walkthrough

See Agies on your own stack.

Book a live demo on our calendar for a guided walkthrough — or use the form to connect your environment and we'll get back to you within one business day.

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  • Enforced in your environment. You control what leaves your network.
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