Get in Touch
AI Security Platform

Proxilion

Every AI Attack Surface. One Security Platform.

AI isn't just chatbots anymore. It's in your IDE, your desktop, your CI/CD pipeline, your agents, and your infrastructure. Proxilion secures every point where AI touches your organization's data but with deterministic enforcement, not probabilistic guessing.

11
Attack Surfaces
4
Enforcement Points
<1ms
Policy Checks
100%
Deterministic

AI Is Everywhere. Security Isn't.

Your employees use AI in ways you can't see: pasting source code into ChatGPT, uploading customer data to Claude, running autonomous agents with unrestricted tool access. Traditional DLP was built for email and USB drives but not for the AI-native world.

Deterministic Enforcement

Policy rules that produce the same result every time. Regex patterns, allowlists, role checks, and cryptographic verification. Cannot be bypassed by prompt injection or social engineering.

  • Same input = same decision, always
  • Sub-millisecond evaluation
  • Zero cost per check
  • Auditable and testable

Behavioral Analysis

Statistical models that detect anomalies over time. Z-score drift detection, kill chain pattern matching, and usage profiling. Complements deterministic rules for unknown threats.

  • Catches novel attack patterns
  • Adapts to organizational baseline
  • Multi-step threat correlation
  • No model calls required

AI Attack Surface Coverage

Every place AI interacts with your organization's data is an attack surface. Here's how Proxilion covers each one.

Desktop AI Apps
Claude Desktop, ChatGPT Desktop, Ollama, LM Studio
Endpoint Agent

Desktop AI apps bypass every network-level control. When an employee pastes customer records into Claude Desktop, no proxy or firewall sees that traffic. Proxilion's endpoint agent monitors clipboard activity, file access, and network connections at the OS level.

  • PII and secrets in clipboard paste detected via pattern matching
  • Shadow AI discovery: unapproved AI apps identified by process monitoring
  • Sensitive file access by AI processes flagged in real time
Deterministic: Regex + Process Monitoring
How it works
The endpoint agent runs as a lightweight system service, monitoring clipboard events and file system access. When data matching sensitive patterns (SSN, credit cards, API keys) is pasted into a known AI process, the action is logged, alerted, or blocked - all before the data ever leaves the machine.
IDE & Code Assistants
Claude Code, VS Code Copilot, Cursor, Windsurf
Endpoint Agent

Code assistants stream your entire codebase to external models. Proprietary algorithms, hardcoded secrets, and internal API endpoints get sent to third-party servers with every completion request. The endpoint agent intercepts and scans this traffic before it leaves.

  • Source code leakage to AI models blocked by content inspection
  • Secrets in prompts (API keys, tokens, passwords) caught by pattern detection
  • Unauthorized model usage enforced via model/provider allowlists
Deterministic: Pattern + Allowlist
How it works
Every API call from the IDE to an AI provider is intercepted at the network level. Outbound payloads are scanned for secrets (AWS keys, GitHub tokens, database URIs) using deterministic regex matching. If the target model or provider isn't on the organization's approved list, the request is blocked before it reaches the internet.
Browser-Based AI
ChatGPT, Claude, Gemini, Perplexity (web UI)
Endpoint Agent

The simplest attack surface, and the hardest to control. Employees copy-paste sensitive data directly into browser-based chatbots every day. File uploads, conversation context, and multimodal inputs all carry risk. Proxilion catches this at the clipboard and network level.

  • Copy-paste of sensitive data intercepted at clipboard level
  • File uploads containing PII scanned before submission
  • Shadow AI usage detected across all browser-based AI tools
Deterministic: Clipboard + Network Scan
How it works
The endpoint agent hooks into OS-level clipboard events and monitors outbound HTTPS connections to known AI domains. When a paste event targets a known AI web app (chat.openai.com, claude.ai, etc.), the content is scanned for sensitive patterns. File upload requests are similarly intercepted and inspected before they leave the device.
Applications Using AI SDKs
Custom apps, SaaS products, internal tools calling OpenAI/Anthropic/Gemini APIs
Proxilion SDK (TypeScript + Python)

Your developers build applications that call AI APIs. Without governance, any prompt could contain customer PII, any response could leak credentials, and any cost overrun goes unnoticed until the bill arrives. The Proxilion SDK wraps every API call with policy enforcement.

  • Prompt injection attempts blocked by deterministic input guards
  • PII in prompts and responses scanned and redacted automatically
  • Model/provider policy enforcement, cost tracking, and rate limiting
Deterministic: Policy Engine Deterministic: Regex Guards
How it works
The SDK intercepts every AI API call at the application layer. Before the request leaves your code, input guards scan for injection patterns, PII, and secrets using regex. A policy engine evaluates authorization rules (which users can use which models, what data can be sent). Responses are similarly scanned before being returned to the caller. Every decision is logged to an immutable audit trail.
AI Agents & Autonomous Systems
CrewAI, AutoGPT, LangChain agents, custom multi-step agents
Proxilion SDK + MCP Gateway

Autonomous agents make chains of decisions without human oversight. A single compromised step can escalate privileges, exfiltrate data across tool boundaries, or trigger destructive actions. Proxilion enforces policy on every tool call in the chain and detects multi-step kill chain patterns.

  • Unauthorized tool calls blocked by role-based policy enforcement
  • Privilege escalation via tool chaining detected by kill chain analysis
  • Excessive autonomy and session hijacking caught by behavioral drift detection
Deterministic: RBAC Policy Behavioral: Kill Chain Detection
How it works
Every tool call an agent makes passes through the policy engine. Deterministic rules check authorization (can this agent call this tool with these parameters?). Meanwhile, a behavioral layer tracks the sequence of actions across the session - detecting patterns like "read credentials, then open network connection, then write to external endpoint" that indicate a kill chain attack.
MCP Tool Calls
Filesystem, database, API, code execution MCP servers
MCP Gateway

The Model Context Protocol gives AI direct access to your systems: databases, filesystems, APIs, and code execution environments. Every MCP tool call is a potential attack vector. The MCP Gateway sits between the AI and your tools, enforcing security before any action is taken.

  • Dangerous invocations (file delete, shell exec, network access) blocked by tool allowlists
  • Prompt injection via tool results detected and neutralized
  • Multi-target attacks and conversation manipulation flagged by pattern analysis
Deterministic: Tool Allowlists Behavioral: Pattern Analysis
How it works
The MCP Gateway intercepts every tool call between the AI model and MCP servers. Each call is evaluated against a deterministic policy: Is this tool allowed? Are the parameters safe? Does this user have permission? Tool results flowing back to the model are scanned for injection payloads that might manipulate the conversation. Kill chain patterns (recon → access → exfiltration) are detected across the session.
CI/CD Pipelines Using AI
GitHub Actions calling GPT, Jenkins with AI steps, automated code review bots
MITM Proxy

CI/CD pipelines run unattended, often with elevated permissions and embedded secrets. When a build step calls an AI API, it may send credentials, proprietary code, or training data to external services. Automated calls can also spiral in cost without anyone noticing. The proxy intercepts this traffic transparently.

  • Secrets and credentials in AI API payloads caught by regex scanning
  • Cost runaway from automated calls stopped by rate limiting
  • Unauthorized model usage in pipelines enforced via provider policies
Deterministic: Proxy + Regex
How it works
The MITM proxy is deployed as a transparent gateway between your CI/CD environment and external AI APIs. All outbound requests to AI providers are routed through it automatically (via environment variables or network config). The proxy scans payloads for secrets patterns, enforces model allowlists, tracks cost per pipeline, and rate-limits automated requests - all without modifying your build scripts.
Server Backends & Cloud Functions
Production apps calling AI APIs, Lambda/Cloud Functions, K8s pods
MITM Proxy

Production services process real customer data. When they call AI APIs, PII can leak in API payloads, secrets can appear in responses, and costs can multiply across thousands of serverless invocations. The proxy provides infrastructure-level enforcement without any code changes.

  • PII in API payloads detected and redacted before reaching AI providers
  • Response scanning for leaked secrets and credentials
  • Per-service cost tracking and compliance enforcement
Deterministic: Proxy + PII Scan
How it works
Deploy the proxy as a sidecar container or network gateway. All AI API traffic from your backend services is transparently routed through it. Outbound payloads are scanned for email addresses, phone numbers, SSNs, credit card numbers, and other PII using deterministic pattern matching. Inbound responses are scanned for leaked credentials. Every request is logged with cost tracking and compliance metadata.
AI-to-AI Communication
Multi-agent orchestration, agent-to-agent delegation
MCP Gateway + SDK

When agents delegate tasks to other agents, trust boundaries blur. One agent's context can leak into another's context. Privilege can escalate invisibly as tasks are delegated. Without governance, automated decisions cascade with no audit trail. Proxilion enforces trust boundaries at every handoff.

  • Uncontrolled inter-agent delegation blocked by trust boundary enforcement
  • Privilege escalation across agent boundaries prevented by policy inheritance
  • Complete audit trail for every automated decision across agent chains
Deterministic: Policy Inheritance Behavioral: Drift Detection
How it works
When Agent A delegates a task to Agent B, the SDK ensures that Agent B inherits only the permissions explicitly granted by Agent A's policy - never more. Each agent boundary crossing is logged as a discrete event. If an agent's behavior drifts from its baseline (using Z-score statistical analysis), the system flags the deviation and can trigger a kill switch.
Model Supply Chain
HuggingFace downloads, LoRA adapters, GGUF files, fine-tuning
Endpoint Agent + SDK

Your organization might be running models that were never approved, downloaded from untrusted sources, or fine-tuned with data that shouldn't have left the building. Proxilion maintains a model registry and enforces that only attested, approved models are used across the organization.

  • Unregistered/unapproved model usage blocked by model registry enforcement
  • Connections to unknown AI endpoints flagged and blocked
  • Training data governance - data sent to providers tracked and audited
Deterministic: Registry + Allowlist
How it works
The endpoint agent monitors network connections to known AI model repositories and API endpoints. Any connection to an AI endpoint not in the organization's approved registry is flagged or blocked. The SDK enforces model identity checks at the application layer - ensuring that every inference call targets an approved, attested model. Fine-tuning data flows are logged for governance.

The Trust Plane

Every security vendor says they can "secure AI." But can they prove it? The Trust Plane is the answer.

Cryptographic Proof for Every AI Interaction

Imagine your auditor asks: "Can you prove that every AI interaction in your organization was governed by policy last quarter?" With most tools, you'd scramble through logs, cross-reference timestamps, and hope nothing fell through the cracks.

With Proxilion's Trust Plane, the answer is a single cryptographic verification. Every enforcement point - endpoint agent, SDK, MCP Gateway, proxy - generates a signed attestation for every action it governs. These attestations form a hash-chained, tamper-evident record that links each AI interaction back to the original policy decision and organizational intent.

This isn't just logging. It's mathematical proof that governance happened. No gaps. No retroactive edits. No "trust us, we checked." The chain of evidence flows from the individual action, through the policy that authorized or denied it, all the way up to the organizational rules that created that policy. An auditor can verify the entire chain independently.

Tamper-Evident Audit Trail
Every log entry is hash-chained to the previous one. Modify or delete a single record and the entire chain breaks- making tampering immediately detectable.
Signed Attestations
Each enforcement point cryptographically signs its decisions. You can prove which policy was applied, what data was scanned, and what action was taken - for every single AI interaction.
Policy-to-Action Traceability
Trace any AI action backward through the chain: which user initiated it, which policy governed it, which organizational rule created that policy, and when it was last updated.
Independent Verification
Auditors don't have to take your word for it. They can verify the cryptographic chain themselves - no special access or trust relationship required. True zero-trust compliance.

No other AI security vendor offers cryptographic proof of governance across every enforcement point. The Trust Plane is what makes the difference between "we have logs" and "we have proof."

Interested in Securing Your AI?

If you're exploring how to protect your organization from AI-related data risks, we'd love to hear from you.

Get in Touch