ARXIMUS AI Runtime Security
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AI Runtime Security

AI SECURITY
Secure every AI action and interaction.

Arximus sits between AI and the systems it interacts with, inspecting traffic, enforcing policy, controlling access and blocking unauthorized actions before they happen.

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AI GATEWAY ACTION CONTROL IDENTITY POLICY CREDENTIALS MCP SANDBOX EVIDENCE
AI ACTION ENFORCEMENT

Control every AI action before it reaches your systems.

Arximus intercepts AI actions before execution, giving you direct control over what AI can access, change, send and run.

Every action is evaluated against identity, permissions, policy, data sensitivity and risk. Arximus can allow, block, restrict, redact, contain or require approval before anything reaches your systems.

SECURITY MODEL

One control plane across the complete AI execution chain.

Arximus follows the full chain from human principal to application, agent, sub-agent, model, tool, credential, resource and resulting action. Security decisions retain that context end to end.

01

Discover

Build an authoritative inventory of AI applications, agents, models, tools, MCP servers, knowledge sources, credentials and data paths, including unmanaged AI activity.

Assets → Ownership → Exposure → Risk
02

Govern

Express deterministic policy around identity, delegation, data classification, tools, resources, destinations, transaction values, environments and human approval.

Identity → Policy → Least Privilege → Approval
03

Enforce

Inspect prompts and responses, intercept tool calls, bind approvals to exact transactions, isolate credentials, contain execution and stop unsafe agent behavior in real time.

Inspect → Decide → Constrain → Execute
04

Prove

Record structured security events, approval chains, policy versions, execution results and cryptographic integrity evidence so every consequential AI action can be reconstructed.

Trace → Evidence → Audit → Accountability
ARXIMUS PLATFORM

Security infrastructure for AI systems with real authority.

Arximus combines inline enforcement, identity-aware authorization, runtime containment, data security, threat detection and tamper-evident evidence in one operating layer.

01

AI Gateway & Data Firewall

Every model interaction enters a controlled, observable security path.

Route model traffic through a provider-neutral enforcement layer with request and response inspection, DLP, secret detection, redaction, retention controls, model routing and structured traces.

  • Prompt & response inspection
  • DLP & secrets
  • Zero-retention modes
  • Provider abstraction
02

Action & Transaction Firewall

Authorize what AI may actually change in the real world.

Intercept tool calls and consequential operations before execution. Evaluate actor, resource, data, destination, risk and policy, then allow, deny, transform, sandbox or require approval. Bind approvals to the exact authorized transaction so altered parameters cannot inherit prior consent.

  • Tool-call interception
  • Parameter binding
  • Human approval
  • Fail-open / fail-closed
03

Identity, Delegation & Credential Broker

Know who the AI is acting for and give it no more authority than necessary.

Preserve the delegation chain from human or service principal through applications, agents and sub-agents. Replace standing credentials with short-lived, narrowly scoped authority issued only after policy evaluation.

  • Agent identity
  • Delegation controls
  • Short-lived credentials
  • Least privilege
04

Policy Engine & Simulation

Deterministic policy remains the authority, not the model being governed.

Define controls across identity, model, tool, data classification, resource, environment, destination, jurisdiction, risk and transaction value. Test policy against historical traffic in shadow mode before enforcement.

  • Policy as code
  • Shadow mode
  • Impact simulation
  • Versioned controls
05

Agent, MCP & Memory Security

Protect the context, tools and persistent state that increasingly shape agent behavior.

Detect prompt injection, tool poisoning, unsafe MCP behavior, RAG and memory poisoning, excessive agency and dangerous delegation. Track untrusted influence through an execution chain and block sensitive downstream actions when context becomes unsafe.

  • Prompt injection
  • MCP security
  • Memory firewall
  • Taint tracking
06

Sandbox & Runtime Containment

AI-generated execution happens inside explicit technical boundaries.

Run code and high-risk operations inside ephemeral environments with bounded CPU, memory, filesystem and network access. Couple containment with behavioral detection, blast-radius analysis and emergency kill controls.

  • Ephemeral execution
  • Network controls
  • Behavioral detection
  • Kill switch
07

Evidence Vault & Forensics

Turn AI activity into verifiable security evidence instead of disposable logs.

Capture structured events for identity, policy, approvals, tool access, data classification and execution results. Protect evidence with append-only storage, cryptographic digests and independently verifiable integrity chains.

  • Forensic traces
  • Approval evidence
  • Integrity verification
  • Control mapping
08

AI Discovery & Agent Manifest

You cannot secure AI systems you cannot see, classify or attribute.

Discover managed and unmanaged AI activity and maintain an inventory of agents, models, MCP servers, tools, knowledge sources, APIs, credentials and data access. Build a versioned manifest for each agent so its dependencies, authority and exposure remain inspectable.

  • Shadow AI discovery
  • Asset registry
  • Agent manifest
  • Supply-chain visibility
09

Security Lab & Continuous Validation

Attack the agent before an attacker does, then keep testing as the system changes.

Evaluate agents against prompt injection, tool poisoning, privilege escalation, data exfiltration, memory poisoning, malicious MCP behavior, unsafe delegation and policy bypass before production. Re-run security evaluations when models, prompts, tools or policies change.

  • Agent red teaming
  • Policy bypass testing
  • Change validation
  • Security scoring
CENTRALIZED ENFORCEMENT

One security layer controls the entire AI execution chain.

Arximus centralizes every critical security decision across AI systems, identity, tools, data, credentials, policy and execution.

Instead of securing each part of the AI stack separately, Arximus brings them into one coordinated enforcement plane. Every interaction and action is evaluated with the full context, so security controls work together before anything is allowed to proceed.

One control plane Full execution context Coordinated enforcement Centralized evidence
RUNTIME DECISION

Every AI action becomes an explicit security decision.

The enforcement path is designed to remain understandable under pressure. Security teams can see why a decision occurred, which controls applied and what the AI actually attempted to do.

01

Observe

Capture the interaction, tool request, execution context and relevant data movement.

02

Attribute

Resolve human, service, application, agent, sub-agent and delegated authority.

03

Inspect

Evaluate content, data classifications, prompt injection, tool risk and behavioral signals.

04

Evaluate

Run deterministic policy against actor, resource, destination, environment and transaction context.

05

Authorize

Allow, deny, redact, transform, sandbox, quarantine, throttle or require approval.

06

Execute

Release narrowly scoped authority and contain execution inside the permitted boundaries.

07

Prove

Preserve the identity chain, policy version, approval, action object, execution result and integrity evidence for reconstruction and audit.

DEPLOYMENT & DATA CONTROL

Deploy Arximus around your infrastructure, security and data requirements.

Choose how and where Arximus runs, from fully managed cloud to private, VPC and hybrid deployment.

Centralize security policy without giving up control of sensitive traffic, data retention or encryption keys. Arximus adapts to your environment while keeping enforcement consistent across every deployment model.

  1. CLOUD
    Arximus Cloud

    Managed enforcement for teams that want the fastest path to centralized AI security, policy and observability.

  2. VPC
    Private Data Plane

    Run the security data plane inside your cloud environment while retaining centralized policy management and control.

  3. PRIVATE
    Fully Private

    Operate Arximus inside customer-controlled infrastructure for private models, restricted networks and high-assurance workloads.

  4. HYBRID
    Hybrid Enforcement

    Apply one governance model across cloud, private, self-hosted and third-party AI systems without collapsing every data path into one location.

  5. CONTROL
    Retention & Key Control

    Choose full, selective, redacted, metadata-only or zero-retention capture, with tenant isolation and customer-controlled key strategies.

SECURITY PRINCIPLES

Every AI system should operate under explicit security control.

Arximus is designed around the assumption that intelligent systems can be useful, compromised, manipulated, mistaken or simply over-authorized. Security controls remain independent from the model.

01

Explicit identity

No consequential action without knowing which human, service, application and agent chain is responsible.

02

Least authority

No permanent privilege where short-lived, action-specific authorization can reduce the blast radius.

03

Deterministic control

The model can propose. Policy decides. Security authority remains outside the system being governed.

04

Deliberate failure

Critical workflows fail closed. Lower-risk workflows can degrade safely according to explicit operational policy.

05

Verifiable evidence

Every important decision should be reconstructable, attributable and protected against silent alteration.

ENTERPRISE ACCESS

Bring every AI action under security control.

Tell us how AI interacts with your models, tools, data, credentials and production systems. We will identify where Arximus can enforce control, reduce exposure and give your security team full visibility over AI activity.

A strong fit for Arximus
  • Your AI systems can call tools, APIs, databases or production infrastructure.
  • You need deterministic control over what agents are allowed to do.
  • You need visibility across AI traffic, identities, data movement and execution.
  • You require private deployment, strong audit evidence or enterprise governance.

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