AI agents act across three planes no single tool can see. Spectis is the only platform that observes all three — external API, internal network, and endpoint — correlates them by identity, and holds it all in one platform you control. Observe by default — enforce by choice.
AI agents reach across external APIs, internal networks, and local endpoints. Secure Access Service Edge (SASE) platforms see the first. MCP gateways see part of the second. Code-level scanners catch a sliver of the third. Nobody sees them together — and that gap is where shadow agents live.
Agent calls to model providers — OpenAI, Anthropic, Bedrock. Your SASE platform sees opaque TLS, never the prompts, tool calls, or decisions inside.
MCP servers reaching databases, directories, Splunk. Deliberately bypassed from inline inspection to avoid latency — so it goes completely unmonitored.
Process-to-process stdio, config files, skills, hooks, credentials — local to the workstation. Code-level scanners glimpse it; none correlate it back.
Each adjacent category was built for a pre-agent world. Even the newest AI-SPM tools, which now reach code-level endpoints, stop at coding agents and source repos — they never touch enterprise SaaS agents, and none correlate across planes. Spectis was designed to join all of them by identity.
| Tooling category | API | Network | Endpoint | Cross-plane correlation |
|---|---|---|---|---|
| SASE / SSE | partial | ○ | ○ | ○ |
| MCP gateway / proxy | ○ | partial | ○ | ○ |
| EDR / XDR | ○ | ○ | partial | ○ |
| AI-SPMcode & coding agents | partial | ○ | code-level* | ○ |
| Spectis | ✓ | ✓ | ✓ | ✓ |
* AI-SPM now reaches coding agents (VS Code, Claude, Codex) and source repos, but not enterprise SaaS agents (M365 Copilot, Copilot Studio) — and none correlate the endpoint back to the API call and the human. Spectis joins all three planes on username × hostname × agent_id.
A telemetry and correlation layer — not a gateway, not a proxy. Nine capabilities, one shared identity graph, held and analyzed in one platform you control.
Scans MCP configs across 21 AI clients — VS Code, Cursor, Claude, Codex, Windsurf, Zed, JetBrains and more — finds running servers, probes tool inventories, surfaces shadow agents. macOS · Windows · Linux.
Every artifact, runtime, and the inferred flows between them. See what an agent can actually reach — endpoint → cloud model, repo → production — scored for blast radius.
Read-only inventory of Bedrock & SageMaker, Azure OpenAI & AI Foundry, and GCP Vertex — guardrails, public endpoints, and who can invoke what.
Tenant feature flags, license adoption, Copilot Studio agents, Graph connectors, sensitivity/DLP posture and usage — the enterprise SaaS surface code-level tools miss.
Crawls GitHub orgs for AI artifacts in source — agent definitions, skills, prompts, leaked tool grants — before they ship to production.
Every agent gets a registered, scoped identity; every action is attributed to both the agent and the human who triggered it — the full delegation chain, audited.
Async behavioral analysis that catches what rules miss — reconnaissance, exfiltration through read-only access, prompt injection, and privilege creep across sessions.
Everything is correlated and retained in Spectis — one source of truth you own. We stream only Spectis's own audit log to your SIEM; your correlated intelligence never leaves the platform.
A deterministic rule pack plus an optional LLM tier scores every artifact — benign, enterprise, suspicious, harmful — with a rationale you can audit.
Spectis observes across all three planes, joins everything by identity, and holds it in one platform you control — nothing in the request path, zero latency, no data handed to third parties.
Spectis is hosted, secured, and run by us — nothing for your team to deploy, patch, or babysit. Connect your AI sources, see and govern everything in one platform, and you're live the same day.
Your platform runs on dedicated, encrypted, single-tenant infrastructure that we operate and monitor. And because Spectis is data-minimized at the source, the sensitive material never reaches us in the first place — we collect metadata, never your secrets or content.
Security, governance, and isolation that satisfy the teams who sign off — without a six-month procurement cycle.
Dedicated single-tenant infrastructure — no shared environments, no co-mingled data, ever.
Connect your identity provider for single sign-on and automated, policy-driven user provisioning.
Scoped roles and a complete, attributable audit trail for every agent and every human action.
Data minimization by design, full audit logging, and GDPR alignment — SOC 2 on the roadmap.
Stream Spectis's audit trail to Splunk, Sentinel, CEF or webhooks for compliance — while your correlated intelligence stays in Spectis.
White-glove onboarding, a named technical contact, and enterprise service-level agreements.
A security product has to be the most secure thing in the room. Spectis is engineered that way — minimal data, modern crypto, least privilege, and enforcement only where you choose.
Observability adds zero inline latency and never touches the request path — no conflict with your SASE or proxy stack. Enforcement intercepts only what you choose to block.
Env var names only, never values. No raw command lines. No file contents unless explicitly flagged.
TLS 1.3, Ed25519-signed commands with replay protection, short-lived agent identities. Argon2id at rest.
Every action attributed to both agent and human. Inspectable verdicts, full audit trail, evidence per alert.
Observability ships today. Compliance and policy enforcement are next — to our knowledge, no single company is building all three in one platform. Here's where Spectis is going.
Intercept and block non-compliant agents, skills, and tool calls in real time. Hooks for Claude, Codex, and VS Code today, plus the MCP policy engine. Coming: a drop-in SDK that brings the same enforcement — and prompt-injection detection — to your production apps.
Connect Claude, Codex, OpenAI, M365 Copilot, and GitHub Copilot for org-wide metrics — adoption, spend, model & tool mix, top users, and trends over time.
Ingest server-side ground truth through the OpenAI and Anthropic compliance APIs, retain conversations, and turn them into analytics — who's using AI, how, and why — with LLM-powered summarization and risk scoring.
The first systematic mapping of MCP configuration paths across every major AI client, and a formal agent-identity model for enterprise security.
We show that SASE, MCP-gateway, EDR, and AI-SPM tools each cover at most one plane of AI-agent activity, formalize a cross-plane identity model, and evaluate Spectis against a reference enterprise environment.
Read the whitepaper →| Tool | API | Net | Endpoint |
|---|---|---|---|
| SASE | ~ | — | — |
| MCP gateway | — | ~ | — |
| EDR / XDR | — | — | ~ |
| AI-SPM | ~ | — | ~ |
| Spectis | ✓ | ✓ | ✓ |
Bring end-to-end agent observability to your enterprise — held, correlated, and governed in one platform you control.