Not automation. Decision Intelligence.
Most "AI security" is a chatbot bolted onto automation. Ours is a team: Lucy, the one agent you talk to, and the named specialists behind her, learned from 35 years of running complex networks. One door, sovereign, human-accountable.
Everyone has models.
Almost no one has the data to teach them.
The biggest wins in AI don't come from generic automation, they come from learning a company's own know-how into the model. The frontier proved it: a system that learned to reason over real code found thousands of flaws humans missed. The lesson for security is the same, the moat isn't the model, it's the experience you can teach it.
Ours is 35 years of operating complex environments across hundreds of customers, millions of real decisions, incidents and resolutions. That is the fuel. Here is how we turn it into intelligence.
▸ Operational Intelligence, built-in, expertise embedded in the platform, not locked behind the managed-service layer. The design principle: centralize the foundation, distribute the intelligence, and make every capability operable by AI workflows, not only by expert humans.
Three ingredients make an AI that learns.
A learning AI needs more than logs. It needs static know-how, live transactional data, and an ontology that connects them by meaning, so agents reason about what's actually happening, not match strings.
Static know-how
35y of playbooks, firewall configs, L3 resolutions, architectures & threat knowledge
Transactional telemetry
Live logs, NetFlow, firewall & proxy events, identity & SASE traffic
Threat & world context
CVEs, MITRE ATT&CK, sovereign threat-intel feeds, asset & business context
The Open Systems Security Ontology
A living knowledge graph that links assets ↔ identities ↔ traffic ↔ threats ↔ resolutions. Static experience and live telemetry become one semantic model the agents can reason over.
Predict
See the incident before it happens
Validate
Tell real risk from the noise
Act
Resolve inside your boundary
▸ Static + transactional + ontology, the three things a genuinely learning AI requires. This is the part competitors can't shortcut.
It replicates the
L3 workflow.
Our Mission Control engineers and tech partners train agents on real ticket data for automated root-cause analysis. "Replicated" means the agent would have executed the exact same steps and tools as a human Level-3 engineer.
- ▸ Trained on logs, firewall & ticket data from 35 years of operations
- ▸ Human-in-the-loop approval boundaries
- ▸ Runs 24/7, escalates when it matters
The questions keeping CISOs up since Mythos.
AI didn't just help defenders, it rewrote the attacker's economics. Here are the three problems every CISO now has, and how we answer them.
The patch race is now hours
AI finds and weaponises flaws faster than you can patch. Our answer: virtual patching at the SASE edge, we neutralise an exploit path on the network before you can deploy the fix.
The false-positive flood
AI scanners drown dev teams, reported false-positive rates near 70%. Our answer: validation grounded in decades of triaged incidents, so only real, reachable risk ever reaches a human. The backlog stops growing.
The flaw is at your supplier
Most exposure now lives in your supply chain, and you can't make them patch. Our answer: we contain at the edge so an unpatched vendor flaw simply can't be reached, and assess critical suppliers continuously, pre-vetted with frontier-lab tooling.
▸ Because we own the network and the security, we can act where others can only alert, the SD-WAN traffic itself becomes an AI asset.
A compound system, not a chatbot in a wrapper.
Model-agnostic, sovereign-first
No single-LLM lock-in. The best model per task, deployable inside your region, so your data stays in your jurisdiction.
Grounded, not guessing
Every answer is grounded on the ontology, real telemetry and historical evidence, with citations, not free-form generation.
Bounded autonomy
Propose → approve → act within limits. Dry-run before apply, instant rollback, full decision provenance.
▸ Proof, not promise: agents are replayed against thousands of historical L3 incidents, did they do what the human did?
Built for the next security era. Roadmap
The threats of the AI era need new layers. These are the positions we're building toward, grounded in the same ontology.
AI Control Tower
RoadmapSoon every team runs its own autonomous agents with their own permissions. We govern them, what they may do, how they behave, and stop them when they drift. Zero-trust, applied to AI itself.
Identity Governance Layer
RoadmapInternal identities are the next front line. A continuous governance layer checks intent and context on every privileged action, so stolen credentials still can't do real damage.
Cyber-Resilience
RoadmapWhen you're hit, speed of recovery beats everything. AI-driven containment limits the spread and restores a verified safe state, resilience as a first-class capability, not an afterthought.
Threat-Intel Sovereignty
RoadmapAs nations restrict intel-sharing across borders, your threat-intel supply chain is both an asset and an exposure. We curate sovereign feeds that respect your jurisdiction, and manage the dependency so it can't become a single point of failure. A geopolitical risk, turned into an edge.
Three questions for every AI vendor.
Ours included.
Nobody should buy "we have AI" on trust. These three questions separate a real system from a wrapper, and here is where we answer each one in writing, no gate, no demo required.
1. What is it made of?
- Blog95% of AI pilots fail. Here is what we learned.Seven lessons from getting AI into production inside a regulated global enterprise.
- BlogBeyond the buzz: real-world benefits of AI in SASEWhat the AI actually does once it sits on live network and security data.
- Agent profileLucy, and the team behind herThe one agent you talk to, and the specialists reporting to her.
2. Who is accountable when it acts?
- BlogWhy "human-in-the-loop" is failingClicking approve on a machine proposal is not the same as owning the decision.
- BlogAI is changing cybersecurity. Accountability decides if it works.Speed without an owner is not security, it is just faster guessing.
- Agent profileArgus, the gateway triageWhat the gateways caught, and whether it mattered, in one sentence.
3. Where does your data end up?
- BlogAI sovereignty: the fourth risk layer you have not auditedYour model provider is a jurisdiction question, not just a procurement one.
- BlogWhere is your data really stored?Follow the data past the marketing map, all the way to the actual region.
- Trust centerCertifications, jurisdictions, boundariesThe audited perimeter the models and agents are allowed to run inside.
▸ Every answer above is public and unpaywalled. When a vendor can only answer these three under NDA, that is an answer too.
What our AI is made of.
The questions a CISO asks the moment somebody says autonomous.
What makes your AI different from everyone's "AI"?
Is this a chatbot with a wrapper around a foundation model?
Does my data train a model that other customers benefit from?
Can the AI change my network on its own?
Which agents can I actually use today?
How do you know the agent is right? What happens when it is wrong?
Where do the models run, and in which jurisdiction?
We already have a SIEM and a SOC. Where does this fit?
How do I check any of this without signing an NDA?
Ask what our AI is made of.
35 years, learned into an ontology and a roster of agents, sovereign, grounded, human-accountable.