An autonomous security agent at the chokepoint of your intranet. It detects, it fixes, and it isolates at the network layer — and gives the CISO one command view of the whole estate. Deploy it on-premises or in your own cloud, with the Monolith Model or the model you already run.
Engineered by Guardian Robotics with Blackfrost AI
A bulletin lands, a CVE drops, a new technique is published — and nothing changes on the estate until a detection engineer gets to it, a ticket clears a change board and a window opens. Meanwhile the attack surface now includes AI agents nobody has written an access policy for. Most security programmes are built on assumptions that were true at the last assessment. The firewall rule that was correct in March. The EDR agent that silently stopped reporting in June. The egress policy nobody has validated since the last migration. Control coverage gets measured constantly; control efficacy gets measured annually, by a consultant, against a scope agreed six weeks earlier.
Meanwhile exploitation of a new CVE is routinely measured in hours. The gap between how fast your attack surface changes and how fast you can prove your controls still hold is where breaches live. Monolith is built to close that gap — and to give you the evidence, continuously, rather than the assurance.
Monolith is not another console that generates work for your team. It runs the loop itself, end to end, and brings you one decision at a time with the remediation already staged.
In-line on the intranet, it maintains a live model of the estate — every asset, every flow, every exposed service — and re-baselines continuously as the environment changes. New vulnerabilities, configuration drift and fresh exposures are picked up as they happen, not at the next scan window.
Each finding arrives with severity, blast radius and the remediation already staged — block this egress, close this port, isolate this host, apply this policy. One approval and Monolith executes it, verifies the change took, and writes the full action record to an evidence store it cannot edit. Pre-approved classes of fix run without waiting.
Attempted compromise of the network or an endpoint is contained first and reported immediately: what was blocked, the source, the technique, and whether anything remains unresolved. You are notified of the outcome — not handed a queue of alerts to investigate.
Everything on the intranet passes a bottleneck: the point between the edge and the estate behind it. Monolith sits on that point. From there it does three things, and it does them itself rather than filing a ticket for someone else to do.
Identity, endpoint and network evidence correlated at the chokepoint into true-positive attack paths in seconds. Controls exercised continuously, intelligence run against your digital twin, silence treated as the finding.
The remediation is staged before you see it: the detection written, the control change prepared, the patch sequenced. You approve, or you have pre-approved the class, and Monolith applies it and verifies it closed.
Isolation happens at the network layer, because Monolith is in-line. A host, an identity, an agent or a whole segment is cut off at the wire in seconds — no endpoint software to trust, no agent that can be killed first, no exception for the device that was never enrolled.
One live view of posture, exposure, every agent on the network and every action Monolith has taken or is waiting to take. Not a dashboard that summarises other tools — the operator’s own view of the chokepoint it manages. Drill from a board-level number to the packet, the identity and the evidence record behind it.
ci-runner-07 cut at wire · egress to unknown ASNIllustrative. Figures are not from a customer estate.
Monolith is a security operator, not another console. It is in-line on day one, needs nothing rolled out to endpoints, and runs the loop by itself. You hear from it when it has done something or needs one decision — in plain language, with every action it took and the proof that the same thing cannot happen again.
One appliance between the edge and the estate. No agents to roll out, no rules to write, no console to learn. It discovers the estate, baselines it, and starts exercising controls the same day.
Pre-approve the classes of fix you are comfortable with and Monolith handles them. Everything else arrives as one decision with the remediation already staged. No queue to work, no dashboard to babysit.
Isolation at the wire in seconds. Credentials revoked, egress blocked, remediation applied and verified — while the incident is still an incident, not a report.
After every incident the chain is replayed against your digital twin, the detection is written and verified, and the control change is applied. The same path cannot be walked twice, and the record shows why.
No alert soup. Every message from Monolith has the same four parts, written for the person who has to decide, not for the analyst who has to decode it.
A build agent (ci-runner-07) began sending data to an address outside the estate it had never contacted, 40 seconds after a credential it holds was used from a device that is not its own.
ci-runner-07 cut at the wire; egress blockedNothing. Containment ran under the pre-approved class unknown egress from a machine identity.
Detection for this path written, run against the twin, confirmed firing. Egress policy for build agents tightened to named destinations. Evidence record #48213.
Illustrative. Not from a customer estate.
We do not open with a deck. In the first week Monolith runs the digital twin against a representative slice of your estate and executes attack profiles from last month’s sector bulletins. We show you the one your controls did not catch — and the detection already written for it.
A SOC drowns in isolated alerts because each one is scored on its own. Monolith reasons across them. A DNS anomaly on one host, a new scheduled task on another, an outbound connection to infrastructure first seen yesterday — individually noise, together a kill chain. It assembles the path, scores it as a whole, and hands you a true positive with the containment already staged.
ISACs, vendor advisories and threat feeds publish intelligence that is generic by design: this actor is targeting your sector with this technique. Somebody still has to turn that into an alert that fires on your infrastructure — and that is weeks of detection engineering most teams never get to. Monolith closes the loop automatically.
TTPs, IOCs, ISAC bulletins, CVE disclosures, vendor advisories, open and commercial feeds. The AI reads every piece of intelligence available and pulls it into your environment continuously.
Each threat is broken down into the concrete sequence an adversary would execute against an estate like yours — initial access, persistence, movement, objective — not a headline.
The profile is executed against a masked replica of your real topology: same assets, same controls, same configuration, none of your production data. It is a safe, exact rehearsal of the attack on you specifically.
If the twin raises nothing, your real estate would have raised nothing. Monolith treats silence as the finding: a blind spot, located and evidenced before an attacker uses it.
It writes the detection logic and the control change for that specific gap, uploads and configures it, then re-runs the profile against the twin to confirm the alert now fires. You are notified of the outcome, not asked to do the work.
This is not a SIEM with a model bolted on. Correlation across every feed, every technique and the specific shape of your infrastructure — at the rate intelligence is published — is beyond any human team. It is exactly what an AI-native agent that reads everything, simulates everything, and acts on its own findings can do before the next bulletin lands. Enablement and speed, not another console.
Every AI agent you deploy is an insider with standing access, machine speed, and no instinct for what it should not touch. Corporate policy written for people has no vocabulary for an actor that acts on behalf of someone, under delegated authority, for a bounded purpose — and might spawn more of itself. This is the hardest problem in the AI environment, and it is Guardian’s core discipline.
In a remote-only organisation nobody has physically met the worker on the VPN. The session that authenticated this morning may be the employee, the employee plus a fleet of agents with their credentials, an agent alone, or a deepfake who passed the interview. ScreenComply establishes that the human is real and acting unassisted.
Monolith attributes every agent to a named identity and a responsible owner, states its authority explicitly — tools, resources, actions, duration — and caps delegation at the authority of the parent task. Agents running on inherited or shared credentials are flagged, not assumed.
Instructing an agent to behave fails the moment it reads something that says otherwise. Monolith checks every consequential action against identity, scope, resource and current policy at the execution boundary — outside the model. Untrusted content cannot authorise an action. If enforcement is unavailable, privileged execution stops.
Conventional anomaly detection assumes a stable baseline. In an AI environment there is none: new agents come online weekly, new data sources are onboarded, new tools are connected, and an agent behaves differently in April because its instructions changed, not because it was compromised. Correlating abnormal behaviour against a baseline that is itself in motion is extremely difficult — and exactly where static rule sets fail.
Monolith re-baselines continuously, per agent, as the estate changes, and correlates against purpose rather than only against history. The question is not just “is this different from last week” but “is this consistent with what this agent is authorised to do” — and that question still has an answer when the first one does not. The same posture extends to cryptographic exposure as post-quantum migration begins: inventory what is in use, flag what will not survive, and treat it as a finding now rather than later.
Identity-bound segmentation, per-action authorisation, egress control and delegation caps — enforced by something the agent cannot reason its way past. How NAC for AI works →
Read more: Fraud at superintelligence scale · The insider threat inside your AI environment · The Controlled Autonomy Standard · AgentGuard
An annual penetration test tells you how you were doing six weeks before the report landed. Monolith exercises your defences continuously, against your live estate, and treats every control as unproven until it has been seen to hold.
Trust is a measurement with a timestamp. Monolith keeps the timestamp current.
Time-to-exploit for a newly disclosed vulnerability is now routinely measured in hours. A remediation pipeline that runs through a ticket queue, a change board and a maintenance window cannot keep pace. Monolith compresses the entire cycle — and it compresses the deployment too. You could build an operator like this internally; most large organisations estimate eighteen months to get a model from approved to allowed-to-act with an audit trail. We built that layer once, published the standard it is built to, and deploy it in a quarter.
Detection, triage, reasoning and remediation planning run on the appliance or in your own cloud tenancy. No third-party API queue, rate limit or outage sits between a finding and a fix.
Threat intelligence, vulnerability data and detection logic update continuously, so what it knows at 3 a.m. is what was published at 2 a.m. — not what shipped last quarter.
The authorisation layer, evidence ledger, approval gates and connectors are already built and already governed. Your engineers go to the problems only you have; we supply the plumbing every enterprise otherwise rebuilds from scratch.
New exposure is correlated against what is actually reachable and exploitable, the fix is staged, and once approved it is applied and confirmed. Exposure windows close in minutes rather than sprints.
“I’m sorry, but I can’t help with analysing this malware sample or reasoning about how it propagates.”
Sample identified. Persistence via scheduled task; C2 beacon to 203.0.113.45 every 300s. Three hosts affected. Containment staged — isolate, kill process tree, block egress. Awaiting approval.
Nearly every AI security product stops at a recommendation for your analyst, because nobody will let the model touch production. And the frontier assistants underneath them decline a great deal of what defenders actually do: analysing malware, reasoning about exploitation paths, writing detection logic for offensive techniques. Things are moving too fast for an operator that apologises instead of acting.
The Monolith Model is engineered with Blackfrost AI specifically for defensive and agentic security work. It operates in a different capacity: it uses every piece of intelligence available to it, reasons about adversary tradecraft the way a practitioner would, and acts at machine speed. The boundary on what it may do is your policy, your signed scope and your approval gates — not a model vendor’s content filter.
Approve a fix from the lift. See exactly what was stopped, and when.
Run Monolith as a purpose-built appliance on the intranet, or deploy it into your own cloud tenancy against the same estate. In both cases the telemetry, the findings and the model itself stay inside a boundary you control — nothing is routed through a vendor’s infrastructure.
Many CISOs have spent a year getting a model approved and are not going to unwind that for a security product. They should not have to. Monolith is the operator layer — the chokepoint, the authorisation boundary, the digital twin, the evidence ledger and the network-level isolation — and the model inside it is a component you choose.
Not sure your model is a fit? Ask for the BYO model briefing — we evaluate it against the twin before anything touches production. Book the briefing →
Speed without control is a different kind of risk, and acting is only defensible if someone can stand behind every action. Monolith’s authority is set by you, enforced outside the model, and revocable in one action. It operates to a published controlled-autonomy standard, and every action lands in an evidence chain nobody on your team can edit — which is what you hand the auditor, the regulator or the board when they ask. Three operating modes; the safeguards below hold at every one of them.
Detects and reports. Takes no action on the environment.
Stages every remediation and executes only on explicit approval.
Executes remediation classes you have authorised in advance. Everything else still waits.
Monolith is engineered with Blackfrost AI, who specialise in the one layer we chose not to approximate: cybersecurity models built for defenders, fine-tuned and released with documented precision, footprint, upstream licence and explicit exclusions.
“The model layer for defensive AI. Not another dashboard.”
Not every site wants an appliance, and not every team needs our model. If you already run a SOC, a cloud estate or your own GPUs, you can license the security model on its own and point it at what you already have — cloud workloads included. If you already have a model, skip this and bring it.
Run the model against cloud and SaaS telemetry: identity, endpoint and network evidence correlated into an incident timeline, without an on-site box.
License the weights and run them on infrastructure you already own. Documented precision, footprint, upstream licence and explicit exclusions.
Separately scoped capacity, runtime, APIs and infrastructure controls if you want the model served without operating it yourself.
| Build | Precision | Footprint | Best fit |
|---|---|---|---|
| Monolith Model Master — flagship, highest benchmark | BF16 | ~4.5 TB | Datacentre and private-cloud deployment; the reference build and the strongest operator we ship |
| Monolith Model FP8 | FP8 | ~2.4 TB | Multi-GPU servers where BF16 will not fit |
| Monolith Model NVFP4 | NVFP4 | ~1.4 TB | The smallest footprint that keeps the flagship architecture |
The Monolith Model is a 2.4-trillion-parameter de-risked release engineered with Blackfrost AI and packaged under the Monolith name. None of these builds fits on a single on-premises box — that is exactly why the appliance runs smaller quantised models locally and the Monolith Model is offered for cloud and datacentre deployment.
Monolith is an appliance, not software you install on a desktop. It is built around one job — sitting at the chokepoint of an intranet and running a security operator there, continuously, with inference inside the box — and every part of it, from the host OS to the fin stack, is there to do that job. Figures are at 32k context, two concurrent agents, and 24 GB held back for the OS and workspaces.
| Specification | Monolith device |
|---|---|
| Role | In-line intranet security operator: detect, fix, isolate at the network layer, with the command view and evidence ledger on the device |
| Placement | Between the edge and everything behind it. Wired network and power; nothing to install on endpoints |
| Memory | 192 GB unified, sized for the Monolith Model on-device with two concurrent agents at 32k context |
| Inference | Local. The model runs inside the box; no upstream dependency, no vendor round trip. Loss of internet does not degrade detection or response |
| Power | 30 W idle · 220 W under load. Passive fin stack, low acoustic footprint; office, closet or rack |
| Trusted compute | Secure boot, signed host OS, measured boot and attested runtime. The device proves what it is running before it is allowed to act, and demands the same of what connects |
| Runtime | Agent-native OS with contained full-system access: disposable snapshot-able workspaces, short-lived capability tokens, egress allow-lists |
| Isolation | Network-level: host, identity, agent or segment cut at the wire in seconds, reversible in one action |
| Evidence | Hash-chained store on the device that the agent cannot edit; exportable for audit |
| Model | Monolith Model (BF16 flagship or FP8 / NVFP4 builds), or your own model served inside the boundary |
| Support | Hardware support, signed updates, and the operating loop maintained by Guardian with Blackfrost AI |
Models tuned for security work · harness as the 24/7 agent runtime · agent-native OS with contained full-system access · hardware built to hold all three. Each layer is built to fit the one above it.
Disposable snapshot-able workspaces, short-lived capability tokens rather than standing credentials, network egress allow-lists, and an evidence store the agent cannot edit.
A portable variant: the same operator with a full-day battery, standalone satellite uplink and solar input in one case, so it can be stood up anywhere.
Already have the hardware? Monolith can also run on GPU workstations or high-memory machines you own. Tell us what you have and we will check the fit. Bring your own hardware →
Why an annual pentest is one data point, and how Monolith supplies the other 364.
Read →Chaining identity, endpoint and network evidence into a verdict in seconds.
Read →TTPs and IOCs run against a digital twin; silence becomes a deployed detection.
Read →The digital-twin loop, step by step, for CISOs and CTOs — including where it is guessing.
Read →Discover, assess reachability, fix and verify as one loop.
Read →Human and machine identity as one problem, enforced outside the model.
Read →Per-action, per-agent authorisation that fails closed.
Read →Two deployment models, one boundary you own.
Read →And why a model built for defenders does the job.
Read →Scale and sophistication shifted at once. Fraud is now a top-tier threat.
Read →Voice and video the average person cannot tell from a human.
Read →$3.04B in BEC losses in 2025. Defending the layer the campaign depends on.
Read →Investment clubs, scam centres and the backstop that passes every check.
Read →Every attack ends in an action. The chokepoint holds, verifies, blocks or isolates it.
Read →Tell us about the estate and the model you run, if you have one. In week one we run the twin against a slice of it and show you a real gap with the fix staged. Then we scope on-premises or cloud deployment and get you a quote.
Thank you. A member of the Guardian team will be in touch within one business day.