Witness AI vs. Prediction Guard

Prediction Guard offers a secure, self-hosted AI control plane with built-in governance enforcement allowing organizations to observe and control AI application and agent behavior. While Witness AI focuses heavily on securing enterprise workforces and applications through network-level visibility and intent-based behavioral guardrails, Prediction Guard differentiates itself through its infrastructure sovereignty across flexible deployments (including air-gapped environments), native agent building, and active runtime policy enforcement that generates exportable proof (such as versioned AIBOMs) to manage the entire AI supply chain.

The core difference between Prediction Guard and Witness AI lies in their fundamental architecture and operational scope: Witness AI functions as a network-level enterprise security firewall and visibility platform, while Prediction Guard is a sovereign, self-hosted AI control plane designed to serve as the core infrastructure layer for deploying, governing, and managing your own AI systems and autonomous agents. Witness AI acts primarily as an intercepting security broker or overlay, integrating with existing firewalls, proxies, and endpoints to observe employee activity, classify intent, and filter bidirectional traffic. While this provides guardrails for broad corporate AI usage, it differs from Prediction Guard’s approach of hosting and orchestrating the AI environment itself.

Prediction Guard embeds governance directly into the deployment infrastructure, allowing software vendors and engineering teams to deploy a completely self-hosted control plane inside their own cloud VPC, on-premises data center, or even in constrained edge and air-gapped environments. Rather than just monitoring traffic flowing across a network, Prediction Guard allows organizations to actively assemble and manage their entire AI supply chain (combining models, tools, and MCP servers behind a unified gateway). It features native, secure agent-building capabilities (like its no-code Agent Forge) that are hardwired into a real-time governance harness. This allows organizations to actively enforce runtime security policies before data ever leaves their sovereign network boundary, while generating continuous, exportable proof of compliance, such as versioned AI Bills of Materials (AIBOMs) and runtime SIEM/SOAR audit logs, to manage risk dynamically rather than treating security as an afterthought.

Evaluation Criteria		Witness AI Capabilities
Real-Time Input/Output Guardrails Detects prompt injections, jailbreaks, PII leakage, and harmful content in real time.	✓ Validates data at the core infrastructure control plane before it ever leaves the network, eliminating external gateway bypass risks.	✓ Uses network-level intent classification and tokenization to intercept and block risks across corporate traffic.
Agentic Activity Monitoring Tracks agent handshakes, tool calls, and behaviors to maintain visibility over autonomous systems.	✓ Monitors all agent tool behaviors, system interactions, and model handshakes directly via an OpenAI and Anthropic compatible API gateway.	✓ Provides comprehensive visibility into external agent server activity and tool calls, mapping actions back to human identities.
Sovereign, Air-Gapped Infrastructure Deploys fully on premises, at the edge, or in a cloud VPC without external SaaS dependencies.	✓ Designed explicitly for highly regulated industries and can run completely air-gapped, ensuring total sovereignty over the AI supply chain.	✕ Built primarily as a cloud-native single-tenant firewall and intercept proxy that relies on integration with existing cloud or network systems.
Exportable AI Governance Proof Generates versioned, exportable compliance artifacts and active bills of materials.	✓ Composes assets into verifiable AI Systems to export versioned CycloneDX AIBOMs and pushes real-time audit logs to your SIEM or SOAR.	− Provides immutable audit trails for compliance reporting but lacks formal support for dynamic, exportable AI Bill of Materials management.
Native No-Code Agent Authoring Built-in tooling allowing non-technical teams to build secure agents rapidly.	✓ Includes an integrated no-code interface to build domain-specific agents that are automatically wired into the underlying governance harness.	✕ Functions strictly as a visibility and protection layer for existing workforces and external agents; does not offer agent building tools.
Infrastructure Control and Asset Kill Switches Centralized administrative controls to instantly pause models, agents, or MCP servers.	✓ Model and infrastructure-agnostic hosting layer gives operators granular rate limits, quotas, and instant kill switches for any AI asset.	✕ Offers intelligent routing and user access revocation based on policy triggers, but does not orchestrate or host the underlying AI models or servers.

✓ Covered

− Partial / unclear

✕ Not covered

What is the core architectural difference between Prediction Guard and Witness AI?

The fundamental difference lies in where the security engine lives.

Witness AI operates primarily as an enterprise network firewall and intercept proxy. It hooks into corporate networks, identity systems, and endpoints to monitor employee behavior, track shadow AI adoption, and inspect bidirectional web traffic.
Prediction Guard is a sovereign, self-hosted AI control plane. Instead of sitting on the network perimeter as an overlay, it deploys directly inside your private infrastructure (cloud VPC, on-premises, or air-gapped environment). It acts as the infrastructure layer where your models, MCP servers, and tools are orchestrated and executed, enforcing security natively from within the AI stack itself.

How does the pricing model of Witness AI compare with Prediction Guard?

Witness AI utilizes a user-based and agent-based pricing structure (with public tiers requiring minimum commitments per enterprise user or per autonomous agent, alongside usage true-ups). As your workforce grows or your fleet of autonomous agents scales, your security software costs scale proportionally.
Prediction Guard offers a predictable, fixed annual software product license. There is no per-seat, per-user, or usage-based pricing. This allows engineering teams and software vendors to scale usage, data throughput, and agent deployments across the entire enterprise without worrying about fluctuating monthly bills or unexpected cost penalties.

Is Witness AI complementary to Prediction Guard’s AI Control Plane?

Witness AI functions effectively as a workforce network visibility and modern DLP tool designed to monitor employee laptops and prevent staff from accessing unapproved public AI tools (like personal ChatGPT accounts) over corporate networks. Prediction Guard does not scan employee web traffic or personal devices; it provides the approved, fully governed, locked-down internal AI systems that teams are meant to build on.

Using Witness AI on endpoints to block unapproved external shadow AI traffic, while standardizing your core engineering, agents, and proprietary data workflows on Prediction Guard's sovereign plane makes them highly complementary. However, if your goal is solely to secure your own developed applications and developer stack, Prediction Guard natively embeds those runtime guardrails into the infrastructure, rendering an external network overlay redundant.

How does Prediction Guard protect against external AI gateway risks?

Recent high-profile security vulnerabilities in centralized cloud tools (such as the LiteLLM gateway exploit) highlight the danger of routing sensitive prompts through external SaaS middle-layers.

Because Prediction Guard is 100% self-hosted and capable of running completely air-gapped, its real-time governance enforcement engine sits entirely inside your own network perimeter. Inputs are scanned for prompt injections, PII leaks, and custom policy violations before any request ever leaves your sovereign boundary to hit an external LLM vendor. If you use entirely self-hosted models, your data never leaves your environment at any point in the cycle.

What exportable compliance proof does Prediction Guard provide for security audits?

Maintaining compliance with AI frameworks (like NIST AI RMF, OWASP Top 10 for LLMs, and ISO 42001) requires verifiable evidence. Prediction Guard allows you to organize your models, agents, and MCP tools into distinct "AI Systems."

From there, you can automatically generate and version exportable AI Bills of Materials (AIBOMs) in industry-standard formats like CycloneDX. This gives you a clear, auditable timeline of your AI supply chain. Additionally, Prediction Guard channels a continuous, real-time log of security events and policy violations directly into your existing security operations center infrastructure (such as Datadog, Splunk, or Crowdstrike) for centralized compliance reporting.

Can non-technical teams build secure AI agents using Prediction Guard?

Yes. Prediction Guard features a built-in, no-code interface called Agent Forge. This allows non-technical business units or domain experts to spin up custom, task-specific agents in minutes without touching code.

Critically, because Agent Forge is natively wired directly into the underlying Prediction Guard control plane, any agent built by business users is automatically bound by the same enterprise guardrails, rate limits, kill switches, and data sovereignty policies configured by the engineering and security teams. This enables organization-wide AI transformation without treating security as an afterthought.

Witness AI vs. Prediction Guard: Network AI Firewall vs. Sovereign AI Control Plane

What is the primary difference between Prediction Guard and Witness AI?

Feature Capability Matrix: Prediction Guard vs. Witness AI

FAQs: Prediction Guard vs. Witness AI