Your firewalls see everything

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but understand almost nothing

Traditional firewalls were built for static rules and signatures. In MSP environments, that leads to three big problems:

• Endless noisy alerts – your team spends hours triaging events that turn out to be harmless.

• No tenant context – the same rule means very different things for a finance customer vs. an industrial plant.

• Slow manual investigation – stitching together flows, users, assets, and threat intel still falls on a human analyst.

  AI Firewall uses an LLM that can reason about those signals in real time and tell you what actually matters.

Turn raw firewall events into clear, prioritized decisions

AI Firewall sits alongside your existing network controls and turns low-level events into high-level, tenant-aware decisions.

Fewer false positives

Aggregate related events and apply LLM reasoning so analysts see incidents, not individual log lines.

Faster triage

Every alert comes with context: suspected technique, affected assets, blast radius, and next best action.

Explainable decisions

Analysts can ask why something was blocked or allowed and get a human-readable explanation to share with customers.

Ingest & Normalise

• Collect firewall logs, NetFlow, identity data and threat feeds across all tenants.

• Normalise into a common event model.

Real-time correlation

• Group related events into “stories” (e.g. suspicious lateral movement, unusual outbound connections).

How it works under the hood

AI Firewall combines line-rate enforcement with an LLM-driven reasoning layer. The packet path stays fast; the brain that evaluates risk gets smarter with every event.

LLM reasoning engine

• A specialised LLM evaluates each story: intent, likelihood of compromise, potential impact, and recommended action.

• It takes tenant profile, asset criticality, and historical behaviour into account.

Action & feedback

• Propose or enforce actions: block, rate-limit, isolate segment, escalate to human.

• Analyst feedback flows back into the system, improving future recommendations.

LLM threat resoning

A real-time LLM trained to interpret network stories, not just packets.
It understands protocol patterns, user behaviour, and threat intel to classify incidents with higher precision than rules alone.

What AI Firewall gives your security team

Policy optimisation & recommendations

The system continuously suggests rule clean-up, new segmentation policies, and anomaly-focused rules based on what it sees in live traffic.

Human-in-the-loop controls

Keep humans in charge: approvals for high-impact actions, clear explanations, and reversible changes so you can deploy AI safely in production.

Tenant-aware policies for MSPs

Model each customer as its own tenant with separate policies, risk appetite, and asset criticality – all managed from a single pane of glass.

Use cases MSPs and MSSPs run on AI Firewall

High-fidelity alerting for SOC

• Correlate low-level events into attack narratives (reconnaissance, lateral movement, exfiltration).

• Prioritise incidents by tenant impact and exposure.

• Feed enriched alerts into your SIEM or ticketing system.

• Generate tenant-specific security summaries with clear narrative, not just charts.

• Explain to customers why something was blocked and what you changed to protect them.

• Extend reasoning across on-prem firewalls, SD-WAN, and cloud security controls.

• Give engineers one consistent view across mixed vendors.

Tenant Security Reporting

Cloud and branch visibility

Built for the way MSP networks actually look

• Multi-tenant from day one – separate customer policies, reporting, and data boundaries.

• Vendor-agnostic – ingest data from existing firewall vendors rather than forcing a rip-and-replace on day one.

• Packaged as a service – position AI Firewall as an “AI-assisted SOC” tier in your managed security offering.

• Aligned with ServiceFabric – share context and automation between ticket queues and firewall incidents.

Safe, governed, and ready for production networks

• Performance-safe design

  Traditional enforcement path stays rule-based and fast; the LLM layer reasons over events, not every packet.

• Data isolation & privacy

  Tenant data is logically isolated; you control what telemetry is sent into the reasoning layer.

• Auditability

  Every recommendation and automated action is logged with full context.

• Deployment options

  Note your real options here: SaaS region(s), private deployment, etc.

See AI Firewall on your network

Share a few details about your customers, firewall stack, and SOC workflows. We’ll walk you through how AI Firewall can add LLM-powered reasoning to your existing defences – without ripping and replacing hardware.