Our platform
Our own automated malware and threat identification system — behavioral, continuously retrained, and built to catch samples that have never been seen before.
Home Platform
Our own detection software. Instead of checking whether a file matches a list of known threats, it watches what programs actually do and flags behavior that does not belong — which is the only way to catch an attack that has never been seen anywhere before.
CerbaCore owns and deploys a complete adaptive AI threat identification platform. It does not wait for a signature to exist, because for a genuinely novel sample no signature ever will.
Signature-based detection answers one question: have we seen this exact thing before? For the overwhelming majority of commodity malware that is enough, and it is cheap. For a zero-day — a vulnerability or a sample with no prior public existence — the answer is always no, and the gate opens.
The industry's response was behavioral detection: stop asking what a file is and start asking what it does. Our platform is built entirely on that premise.
The agent captures syscall sequences, memory operations, process lineage, file activity and network behavior. Not file hashes — the raw behavioral substrate.
Raw events become behavioral sequences: what this process did, in what order, in what context, and what it did next. A sequence is far harder for an adversary to alter meaningfully than a file hash, because changing it means changing what the malware actually accomplishes.
Three model families score every sequence, deliberately disagreeing with each other:
Scores are combined into a weighted verdict with a confidence band. Confidence decides the response: high confidence acts automatically, middle confidence contains and escalates, low confidence records and watches. A single model is never allowed to trigger destructive action alone.
Process termination, host isolation, credential revocation, and — through network integration — quarantine at the router.
Verdicts and analyst adjudications feed back as labeled training signal. The environment model adapts as your estate changes, which is what stops false positives climbing as the business evolves.
No product does, and any vendor who tells you otherwise is selling something. What behavioral detection genuinely provides is this: an attack that exploits an unknown vulnerability still has to do something observable afterwards — escalate, persist, move, collect, encrypt or exfiltrate. That post-exploitation behavior is what we detect, and it does not require prior knowledge of the vulnerability.
The realistic claim is a dramatically shortened window between first execution and detection, not immunity. A defense architecture that assumes some attacks will succeed — and is built to contain them fast — outperforms one that assumes prevention is complete.
Zero-day attack prevention is sold as though the exploit itself can be stopped. Usually it cannot — the vulnerability is unknown by definition. What AI-powered threat detection can do is compress the window between execution and containment from days to seconds, which is the difference between an incident and a breach.
An architecture that assumes some attacks will succeed, and is built to contain them in seconds, outperforms one that assumes prevention is complete.
A model you cannot interrogate is a model you cannot defend to an auditor, a regulator or a board.
The exploit itself is frequently invisible — a memory corruption in a parser, over in microseconds, producing no file and no process. What follows is not invisible. The attacker has to establish control, and every step of establishing control is observable.
| Stage | Signature coverage | Behavioral coverage | Observable signal |
|---|---|---|---|
| Exploit execution | None | Partial | Anomalous memory in a parser process |
| Shellcode / stage 1 | None | Strong | Execution from unbacked memory |
| Process spawn | None | Strong | Lineage that has never occurred on this host |
| Persistence | Weak | Strong | Service, task or autorun write |
| Credential access | Partial | Strong | LSASS or keyring read by an unusual process |
| Lateral movement | Partial | Strong | New auth pattern from a host that never did it |
| Static analyzer | Behavioral sequence | Environment novelty | |
|---|---|---|---|
| Input | File structure, entropy, imports | Ordered syscall and API sequences | Per-host and per-role activity profile |
| Trained on | Large malicious and benign corpora | Known attack technique chains | Your estate only |
| Catches | Commodity and repackaged malware | Technique regardless of binary | Anything abnormal for this host |
| Misses | Fileless, living-off-the-land | Genuinely novel technique | Malicious activity that resembles normal |
| False positives | Low | Moderate, tunable | Higher early, falls as the baseline matures |
| Cost per event | Microseconds | Milliseconds | Milliseconds, and the most memory |
The three are chosen to fail differently. Living-off-the-land defeats the static analyzer but lights up the sequence model. A novel technique the sequence model has never seen still looks abnormal to the environment model. Ensembles work only when the members are genuinely uncorrelated, which is a design constraint, not an accident.
| Band | Condition | Action | Reversible |
|---|---|---|---|
| Critical | Two or more models agree, high score | Terminate, isolate host, snapshot memory | Yes — release is manual and one click |
| High | One model high, others corroborate | Terminate process, alert immediately | Yes |
| Medium | Single model, moderate score | Allow, capture full telemetry, alert | n/a — nothing was blocked |
| Low | Weak signal | Record only; feeds the baseline | n/a |
No single model can trigger a destructive action alone. That rule exists because a single model having a bad day should never take a production fleet offline, and it is the specific failure that has burned the industry publicly more than once.
Continuous retraining on your environment creates a theoretical path for an adversary with long dwell time to normalize their own activity. We mitigate it three ways: the environment model can lower a score but never authorize an action on its own; training windows are bounded so no single period dominates; and analyst overturns carry more weight than automated labels. It is a real risk and we would rather describe our mitigations than pretend it does not exist.
| Metric | How it is measured | What good looks like |
|---|---|---|
| Detection rate | Against a sample set you supply | Compared openly to your incumbent |
| False positives | Per 1,000 endpoints per week, after tuning | Low enough that alerts get read |
| Time to verdict | Execution to decision, measured on-host | Sub-second for local decisions |
| Resource envelope | Sustained memory and CPU under load | Within the contracted ceiling |
| Explainability | Analyst can state why it scored as it did | Every verdict, every time |
Bring us the estate you actually have — mixed OS fleets, ageing routers, unmanaged IoT — and we will tell you what can be unified under one agent and what needs custom work.
