Hosted
One line to install. We manage the infrastructure for you.
AWS Google Cloud
A new foundation for
systems security
Integration
Integration in minutes
On-premise
Deploy on your cloud in your environment. No outside internet connection required.
Linux Docker Kubernetes
Open data
You have full control and access to your data. The schema is open so the record plugs directly into your data lakes and agentic workflows. Archive it and store it on site for as long as you need.
ClickHouse Kafka PostgreSQL Avro Parquet
Visibility
You're flying blind
Bitbison's record is audit-grade and causally complete. It holds every process, file and connection and the cause-and-effect edges between them. The best agent on the market records 13× fewer events at 12× the CPU. Detection improves because it runs over whole chains instead of fragments. It also works retroactively. When new intelligence lands you re-examine history. When a compromise surfaces months later you go back and scope the impact. Bitbison logs everything, all the time, so you know exactly what is happening on all your servers.
CPU utilization on production host
Evidence
Prove what did not happen
After an incident you need to show what did not happen. Counsel and regulators ask which credentials were read and which data left. With partial logs you cannot rule anything out so you end up disclosing more than what was actually taken. Bitbison's record is complete. If a query over the record finds no access then there was no access. Every conclusion links back to the recorded events behind it.
- Prove that a credential was never read
- Disclose exactly what was affected
- Evidence fit for counsel, regulators and customers
"This is the only solution that can tell me what did not happen on my systems."
Head of Security Engineering, design partner at a large semiconductor company
Policy
Behavioral means stateful
Stateful policies need history and current state. Neither exists when runtime observability is below 1% of what actually happens on a system. Bitbison records the history in full and mirrors system state in real time at the CPU cost measured above. Policies evaluate against the live mirror so a rule reads the way you would state it. This is also why false positives fall. A pattern fires on anything that resembles it. A rule that checks the actual chain of events fires only when the intent is violated. The examples below show the difference.
- Policies see past behavior and component interactions
- Policy by origin with no allowlist to evade
- Enforce inline from observe to block
Policy examples
Select a policyThese policies can only be written against a complete causal record.
“Only permit execution of software installed through official, trusted repositories.”
Other products approximate this rule by watching a short list of suspicious directories such as /tmp. Even off-the-shelf bots avoid those directories so the watch is useless in practice. In one of our red team exercises a breached server had malware dropped straight into a service's own directory. Every other solution in place stayed quiet. Bitbison raised the alert because no trusted actor had installed the file. Where a file sits on disk is irrelevant so there is no allowlist to evade.
No allowlist to evade
“Only Ansible, driven from the bastion, may write a config a network-facing service loads.”
File integrity tools record that a config changed and which user and program wrote it. An attacker with code execution can write as that user or through that program so the attributes match. This policy checks the history of the write itself. The change must trace back through an Ansible process to a connection from the bastion. A write with any other history raises an alert that carries the complete chain.
Violations carry the full chain
“No credential may leave the host it was read on.”
Egress rules check connections. A secret leaving over an allowed connection raises nothing. This policy follows the data itself. A secret read by one process and sent by another after re-encoding is still one recorded chain. The alert fires no matter which channel the bytes leave on.
Lineage tracked across processes
Logos
Automated analysis backed by formal coverage
Detection without evidence is useless. Alerts arrive faster than analysts can investigate them. A language model alone is not reliable where the conclusion must be correct. Logos is Bitbison’s automated analyst. It investigates every alert against the complete record. It walks the causal graph, gathers the evidence and writes the conclusion. Logos is built on a formally specified model checker that holds each investigation to complete coverage of the relevant system interactions. The checker computes what has not been examined and sends the analyst back until nothing is left out. Every conclusion comes with its reasoning and its evidence.
- Every alert investigated and none triaged away
- Coverage enforced by a formally specified model checker
- Conclusions carry structured replayable evidence
In your stack
Runs alone or alongside your stack
You do not have to replace anything. Run Bitbison standalone or alongside your existing CNAPP, CSPM or EDR deployment. Alerts from those tools become entry points. Logos adjudicates each one against the record and returns it with the full chain attached or ruled out. Your own AI agents query the same record over MCP (Model Context Protocol).
- Ingest alerts from the tools you already run
- Every signal returned with the full chain or ruled out
- Agents query the record over MCP
Datadog CrowdStrike Elastic Defend GitHub GitLab Slack Jira
Compliance
Beyond audit compliance
We got you covered
Bitbison covers host-side compliance criteria with one agent and one record instead of a stack of point tools. The evidence is generated from what actually happened on the host rather than from configuration claims. The underlying events are retained and available for audit.
User and session auditing
Network activity monitoring
Behavioral runtime defense
Vulnerability scanning
CIS benchmark auditing
Full system observability
Active response
File integrity monitoring
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