AI Surface Intelligence

Map every AI surface in your enterprise.

Wingback continuously maps the agents, models, MCP servers, and inference paths in your AI stack, then scores each for risk and routes findings to the team that owns the fix.

continuous
surface mapping
30+
surface categories
auto
owner routing
wingback / discovery — inventory
489 assets tracked · 12 owners · last refresh just now
124
Agents
18
Models
47
Integrations
300
Data sources
Asset
Owner
Class
Score
checkout-agent (prod)
@payments
Agent
82
billing-integration
@platform
Integration
61
support-knowledge
@support
Data
88
sales-qa-bot
Agent
44
kb-search-tool
@platform
Tool
71
The AI surface

Map every AI surface — cloud, code, MCP, and laptop.

Other tools see the AI you registered. Wingback maps the AI that's actually running, across model providers, MCP servers, inference paths, and workstations. The surface, mapped continuously, the moment it changes.

Cloud connector
AWS · GCP · Azure
{}
Repo scanner
GitHub · GitLab · Bitbucket
Endpoint sensor
macOS · Windows · Linux
Wingback inventory
scored · owned · watched
INFRASTRUCTURE

AI in your cloud

Scanning every account for AI services already in use.

  • ·Bedrock agents and models
  • ·Azure OpenAI deployments
  • ·Vertex AI workloads
  • ·Self-hosted LLMs (vLLM, Ollama)
  • ·SageMaker endpoints
  • ·Vector stores (Pinecone, Weaviate, Qdrant)
  • Misconfigured rate limits, exposed endpoints
SOFTWARE

AI in your code

Reading every repo for AI calls, agents, prompts, and tool configs.

  • ·OpenAI / Anthropic SDK calls
  • ·LangChain · AutoGen · LlamaIndex agents
  • ·Hard-coded system prompts
  • ·MCP server registrations
  • ·Tool & function definitions
  • ·Model APIs in serverless functions
  • Over-broad permissions in commits
ENDPOINT

AI on your laptops

Watching engineering workstations for AI tool sessions.

  • ·ChatGPT · Claude browser tabs
  • ·Cursor · Claude Code · Copilot sessions
  • ·Local MCP servers (STDIO)
  • ·Personal API key usage
  • ·Local Ollama · LM Studio
  • ·Browser extensions calling LLMs
  • Data leaving to unsanctioned providers

How Wingback Secures Agentic AI

Wingback provides intent-to-execution security for AI agents, models, and MCP connectors so you can trace actions, detect drift, and contain threats as agents scale.
Discovery
Protection
Control
Assurance
Engineering

Monitor and secure your cloud infrastructure across major providers. Wingback discovers AI workloads and ensures secure configurations.

12Cloud Accounts
847Resources Scanned
99.2%Compliance Score

Scan repositories for vulnerabilities, secrets, and insecure AI patterns. Prevent prompt injection vectors in code.

234Repositories
15Secrets Found
1.2KScans/Day

Secure MCP server connections and tool invocations. Control which tools agents can access and monitor all interactions.

28MCP Servers
156Tools Managed
45KRequests/Day

Integrate with your existing security and observability stack. Stream events, correlate threats, and respond in real-time.

3SIEM Connectors
1.5MEvents/Day
< 30sAlert Latency

Native support for popular AI agent development frameworks. Secure LangChain, AutoGen, CrewAI and custom agents.

89Active Agents
12Frameworks
99.8%Uptime

Protect agent interactions with enterprise SaaS applications. Control data access and prevent exfiltration.

18SaaS Apps
342API Policies
0Data Breaches

Secure connections to LLM providers and model endpoints. Prevent prompt injection and monitor token usage.

6LLM Providers
2.3MTokens/Day
127Blocked Attacks

Secure AI training data and vector stores. Wingback protects data in Databricks, Snowflake, Pinecone, PostgreSQL, and other data platforms.

56Data Sources
89Access Violations
2.1BRecords Protected
Integration depth

Built to see your entire AI stack.

Wingback ships with native discovery for every major AI platform, framework, and tool — so you don't write connectors for the things you already pay for.

Foundation models
AI applications
Agent frameworks
Cloud & infrastructure
Coding agents
MCP ecosystem
Open-source agents
▸ native discoveryHover any tile to pause the scroll and read the name. Add a custom integration in a day; nothing here is a connector you'd have to build.
Shadow AI, found

What AI is running that you don't know about?

In every first scan, Wingback surfaces AI assets the CISO can't account for. Here's what shows up most often — and how we found it.

CRITICAL

Senior PM's personal ChatGPT, pasting strategy docs

Three times a day for the past two weeks. Account ties to a private email; prompts sit in the training-eligible tier.

▸ Detected via endpoint sensor
CRITICAL

Bedrock agent in a dev account with prod credentials

Spun up during a hackathon in March, never decommissioned. Read access to production buckets including customer PII.

▸ Detected via cloud scan
HIGH

MCP server with `filesystem:write` added last Tuesday

Internal MCP that previously had read-only access. PR approved without flagging the permission expansion.

▸ Detected via code scan + drift comparison
HIGH

Cursor session reading from `customer-data-prod`

An engineer's Cursor instance has been reading customer records for the past week. Prompts cached in the provider's training-eligible tier.

▸ Detected via endpoint sensor
MEDIUM

Self-hosted Llama-3 with no authentication

Deployed to an instance during a POC. Anyone in the VPC can hit the endpoint. No rate limit; no audit log.

▸ Detected via cloud scan
MEDIUM

Third-party MCP server with no data-processing agreement

A team plugged in a vendor MCP that proxies through an unknown infrastructure provider. Not on the approved-vendor list.

▸ Detected via runtime telemetry
What we surface

What does Wingback know about each asset?

Discovery isn't just a taxonomy — it's a profile. Every AI asset Wingback finds carries configuration, exposure, change-history, and ownership signals that drive the score and the remediation.

Agentsclass
Examples
Production agentsBedrock agentsCode-side prototypesEndpoint sessions
Signals tracked
  • ·owner & approval state
  • ·model + version + tool inventory
  • ·system prompt fingerprint
  • ·permission scope
  • ·runtime behaviour pattern
  • ·change diff (30 d)
Modelsclass
Examples
Foundation modelsEmbedding modelsFine-tunesSelf-hosted
Signals tracked
  • ·provider & version
  • ·sanctioned status
  • ·usage by agent
  • ·governance score
  • ·data-processing agreement on file
  • ·training-tier policy
Integrationsclass
Examples
MCP serversTool plug-insWorkflow connectorsCustom integrations
Signals tracked
  • ·permission scope
  • ·approval state
  • ·runtime usage
  • ·vendor approval & DPA
  • ·schema changes
  • ·downstream services touched
Data sourcesclass
Examples
Knowledge basesVector storesPrompt librariesDocument caches
Signals tracked
  • ·sensitivity class
  • ·agents that read it
  • ·ingestion source & freshness
  • ·PII/PHI/IP categories
  • ·retention & residency
  • ·access pattern anomalies
Drill into any asset, see the full picture
wingback / discovery — asset detail
asset
checkout-agent (prod)
owner
@payments
score
82▲ +6
refreshed
12 s ago
Configuration
Modelclaude-opus-4-7· approved
Prompt fp3a7c9b
Tool inventory4 integrations
Permissionspayments:read · refunds:create
Behaviour & exposure
Customer datayes· PII, payment
Outboundinternal only
Tool-call rate12 / min· normal
Drift events (30d)2· resolved
Ownership & governance
Owner@payments
Approver@security
DPA on fileyes
On-call@payments-oncall
posture trend · 30 d
Routed to @payments · response SLA 4 h
Lineage

See how AI connects to your business.

Discovery isn't a list — it's a graph. Wingback maps every dependency, so you can answer the questions you actually get asked: which agents touch PHI, which workflows depend on a deprecated model, which integration would break if a vendor goes down.

wingback / discovery — lineage
AGENTSMODELSTOOLSDATASAGENTcheckout-agentAGENTsupport-botAGENTsales-qaAGENTbilling-reportsMODELclaude-opus-4-7MODELvoyage-3MODELgpt-4oTOOLstripe-toolsTOOLkb-searchTOOLfile-readTOOLslack-mcpDATAcustomers (PII)DATArefund-policyDATA · PHIticket-historyAgentModelToolDataPHI dependency chain
Use the graph

Which agents read customer PHI?

→ 3 agents: support-bot · sales-qa · billing-reports
Use the graph

Which workflows would break if Voyage v3 was deprecated?

→ 2 agents: support-bot · sales-qa — about 14% of monthly volume
Use the graph

Which integrations are reachable from agents touching prod?

→ 6 integrations · 2 without a current DPA
Posture, calibrated

What should we fix first?

Every asset arrives in the inventory with a calibrated 0–100 score. The score combines four factors so engineers can sort by score and fix the worst first — no triage meeting required.

82
/100 · posture
▲ +6 this week
▸ routed to @payments · SLA 4 h
Factor weights · checkout-agent (prod)
Configuration30%
Permission scope, exposed surfaces, default-safe vs. default-permissive.
Change history25%
Recent drift, schema or model swaps, version regressions in the last 30 days.
Exposure25%
Whether the asset touches production data, customer traffic, or downstream services.
Ownership clarity20%
Is there a named owner, an approval trail, a team on call for incidents.
Pre-mapped toISO 42001NIST AI RMFOWASP LLM Top 10EU AI ActMITRE ATLASEvery finding tagged at discovery time.
In flight

Watch your inventory build itself.

As collectors find new assets, they appear in the list and pick up a risk score in seconds.

IN-FLIGHT · newly discovered
assets 489owners 12high-risk 47
endpoint
ChatGPT browser session · k.ortiz
cloud
Bedrock agent · sales-prod-eu
integration
MCP integration · stripe-tools
data
Knowledge base · support-runbooks
endpoint
Claude Code · j.smith / mbp-22
model
Bedrock model · claude-opus-4-7
inventory
scored · owned
▸ k.ortiz / chatgpt-web · scored 28 · routed to @eng~12 s ago
assetownerscore
checkout-agent (prod)
Agent
@payments
82
stripe-tools
Integration
@payments
79
support-knowledge (PHI)
Data
@support
88
sales-qa-bot
Agent
44
j.smith / claude-code
Endpoint
@eng
39
k.ortiz / chatgpt-web
Endpoint
@eng
28
Why this is different

We map the AI that's actually running.

Most discovery tools list the AI assets you intended to deploy. Wingback finds the AI that's actually running — by watching the cloud, the code, the engineering workstations, and the runtime calls. The result: an inventory that reflects what your business actually depends on, not what got registered last quarter. Your forward-deployed engineer tunes the collectors to your specific stack (proprietary frameworks, in-house MCP servers, the experimental cloud account no one remembers), so the map matches your reality and not a vendor's assumptions.

See what's actually in your stack.

Connect your cloud and code; you'll have a scored inventory in under an hour.

Request a discovery scan