2 false positives. 1 real threat missed.1 real detection with evidence.
"Under peak loads, the best existing audit systems lose over 90% of the data, while slowing workloads by 2× to 8×."
Sekar et al., eAudit, IEEE Symposium on Security & Privacy, 2024
Event coverage vs. resource cost measured in production
coverage →
Alternatives
Bitbison
Orders of magnitude more coverage at a fraction of the cost
CPU + memory cost →
"The average breach took 194 days to identify, and 258 days to fully contain."
IBM, Cost of a Data Breach Report, 2024
"54% of intrusions were discovered by an external party - not the organization itself."
Mandiant, M-Trends, 2024
"SOC analysts report that as many as 99% of the alarms raised by signature- and rule-based tools are false positives."
Alahmadi et al., “99% False Positives”, USENIX Security, 2022
Causal policy
policy"managed-config":
scope$f:fspathwhere$f⇒_:exec⇒_:listen# every config a listening service loadsallowbastion.corp:conn⇒ansible:exec→[write] $fdeny_→[write] $f:
alert critical, evidence: full chain
“Only Ansible, driven from the bastion, may write a config that a network-facing service loads. Anything else alerts — with the complete chain as evidence.”
Everything here except the arrows exists in today's rule languages — and attributes can be spoofed. ⇒ asserts the write provably flowed from the bastion: a recorded fact rather than a spoofable attribute. That one primitive is what no other platform can express.
What you see today.
Existing runtime security watches a few hundred coarse-grained events an hour and misses over 99% of what actually happens. You cannot detect or investigate what was never observed.
What is actually happening.
On the same host, over the same hour, tens of thousands of threads, processes, files, connections and other system objects interact with one another. Almost none of it is analyzed or recorded.
Bitbison sees what is actually happening.
No sampling, no blind spots: the full causal graph of what actually happened, continuously, at production scale.
Total observability used to be imis now possible.
Production build server
×13
Agent A56,587 events
Bitbison722,683 events
cpu2.4%0.72%
mem441 MB279 MB
Production web service
×10,537
Agent B32 events
Bitbison337,192 events
cpu0.53%0.32%
mem230 MB278 MB
Production control plane
×189
Agent C1,130 events
Bitbison213,657 events
cpu0.50%0.36%
mem1,191 MB275 MB
Attempted full capture
Agent D50% captured
Bitbison100% captured
cpu100%6%
disk9.2 GB80 MB
Bitbison against industry leaders on real production servers.
Total capture meant unbearable storage, runaway CPU and dropped events, the performance wall that forced the industry into architectures that could only see a fraction of reality. We eliminated the bottleneck, at production scale.
Detection is a dead end.
In the absence of high-fidelity data, you cannot determine what did or didn't happen on your systems. You are left reconstructing events from whatever data happens to exist. These dead ends come from an architecture that was never built to capture enough.
Bitbison logs everything, all the time.
This dead end cannot happen. The complete causal chain behind every alert is already recorded.
Detection is also ineffective.
Signatures, rules, even "behavioral analysis": it is still pattern matching on discrete, disconnected events. Real threats are chains of cause and effect and a pattern at best only approximates a chain: it misfires on benign activity and misses anything it has not seen before.
We rebuilt policy around cause and effect.
On the causal graph, the policy is the intention, stated directly and applied to the whole system, with no evadable allowlists or blocklists to maintain.
The modern security platform.
Autonomous agents, supply chain attacks and commoditized 0-days do not follow patterns that can be enumerated in advance. The tooling built on those patterns has not kept up. Bitbison is a new foundation for runtime security.
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Security you can verify.
The security platform for the modern threat landscape.
Existing runtime security watches a few hundred coarse-grained events an hour and misses over 99% of what actually happens. You cannot detect or investigate what was never observed.
Industry standard200events / hour / host
What is actually happening.
On the same host, over the same hour, tens of thousands of threads, processes, files, connections and other system objects interact with one another. Almost none of it is analyzed or recorded.
Bitbison sees what is actually happening.
No sampling, no blind spots: the full causal graph of what actually happened, continuously, at production scale.
Bitbison95,481events / hour / host
Total observability is now possible.
"Under peak loads, the best existing audit systems lose over 90% of the data, while slowing workloads by 2× to 8×."
Sekar et al., eAudit, IEEE Symposium on Security & Privacy, 2024
Production build server
×13
Agent A56,587 events
Bitbison722,683 events
cpu2.4%0.72%
mem441 MB279 MB
Production web service
×10,537
Agent B32 events
Bitbison337,192 events
cpu0.53%0.32%
mem230 MB278 MB
Production control plane
×189
Agent C1,130 events
Bitbison213,657 events
cpu0.50%0.36%
mem1,191 MB275 MB
Attempted full capture
Agent D50% captured
Bitbison100% captured
cpu100%6%
disk9.2 GB80 MB
Bitbison against industry leaders on real production servers.
Total capture meant unbearable storage, runaway CPU and dropped events, the performance wall that forced the industry into architectures that could only see a fraction of reality. We eliminated the bottleneck, at production scale.
Detection is a dead end.
In the absence of high-fidelity data, you cannot determine what did or didn't happen on your systems. You are left reconstructing events from whatever data happens to exist. These dead ends come from an architecture that was never built to capture enough.
Bitbison logs everything, all the time.
This dead end cannot happen. The complete causal chain behind every alert is already recorded.
Detection is also ineffective.
Signatures, rules, even "behavioral analysis": it is still pattern matching on discrete, disconnected events. Real threats are chains of cause and effect and a pattern at best only approximates a chain: it misfires on benign activity and misses anything it has not seen before.
We rebuilt policy around cause and effect.
On the causal graph, the policy is the intention, stated directly and applied to the whole system, with no evadable allowlists or blocklists to maintain.
Causal policy
policy"managed-config":
scope$f:fspathwhere$f⇒_:exec⇒_:listen# every config a listening service loadsallowbastion.corp:conn⇒ansible:exec→[write] $fdeny_→[write] $f:
alert critical, evidence: full chain
“Only Ansible, driven from the bastion, may write a config that a network-facing service loads. Anything else alerts — with the complete chain as evidence.”
The modern security platform.
Autonomous agents, supply chain attacks and commoditized 0-days do not follow patterns that can be enumerated in advance. The tooling built on those patterns has not kept up. Bitbison is a new foundation for runtime security.
Bitbison's record is audit-grade and causally complete. Detection runs over whole chains instead of fragments and works retroactively. Stateful policies evaluate against a live mirror of system state so a rule reads the way you would state it. Bitbison investigates every alert and every conclusion comes with its evidence.
Stateful policy with no allowlist to evade
Every alert investigated with the full chain attached
The highest-impact supply chain attacks compromise the build itself. xz, SolarWinds and event-stream all shipped CVE-free. Bitbison records every interaction of the entire build and judges it against a semantic model of what the build was expected to do. Tampering is caught before it ships.
Detects build compromise that ships CVE-free
Every build captured in full and audit ready
Whole-system integrity across runners, caches and toolchains