Most security tools use AI to guess what's malicious — and get it wrong 25% of the time. We use formal methods to detect with certainty, then use AI to explain what happened, assess impact, and recommend response. The detection is mathematical. The analysis is assistive. You get zero false positives and intelligent context.
Every detection is deductive, not probabilistic. There is no confidence score. No tuning threshold. No ML model to retrain. If a rule fires, the condition definitively occurred in your environment.
Kernel-level sensors observe system activity at the lowest possible layer. Events flow through the detection pipeline in under 1ms at p99. Attacks are identified as they happen — not minutes or hours later from log correlation.
327 detection rules mapped across all 12 MITRE ATT&CK tactics — from initial access through impact. Coverage validated against the official ATT&CK Enterprise dataset. Write custom rules in a purpose-built detection language.
A lightweight agent runs on every node in your infrastructure. The detection console aggregates and correlates events across thousands of machines in real time, surfacing multi-host attack chains that single-node tools miss.
Every detection includes the full causal chain — which process spawned which child, which file was modified, which network connection was opened. No manual reconstruction. The chain is built automatically from the live system model.
Detect attack sequences that span minutes or hours. Privilege escalation followed by lateral movement followed by data exfiltration — each step correlated across time and across hosts, with configurable detection windows.
Detection policies are cryptographically signed. The agent verifies signatures before loading any rule. Tampered or unauthorized policies are rejected — even if an attacker gains write access to the policy directory.
The agent monitors its own integrity. If an attacker attempts to kill the process, disable sensors, or modify detection rules, the system detects the tampering attempt and alerts independently. Defense in depth, by design.
Bare metal, virtual machines, containers, Kubernetes. Systemd service, DaemonSet, or sidecar. On-prem, cloud, hybrid, or fully air-gapped. One agent binary, one configuration file, one detection engine.
Under 3% CPU overhead at 1M events per second. Under 50MB memory footprint. No disk I/O for detection — everything happens in-memory. Designed for production workloads where performance is non-negotiable.
Every operation is available via API — deploy policies, stream detections, query fleet state, manage agents. Integrate with your existing SIEM, SOAR, ticketing, and incident response workflows.
Update detection rules without restarting agents or dropping events. New policies are compiled, verified, and atomically swapped in. Zero downtime. Zero detection gaps during policy updates.
When detections fire, AI synthesizes the chain of events into a plain-language incident narrative. What happened, which hosts are affected, and why it matters — written for analysts, executives, and compliance teams.
AI correlates detections across the fleet to determine scope. How many hosts are compromised, which clusters are affected, whether the attack is automated or manual, and whether containment is still feasible.
Actionable next steps generated from the specific attack pattern. Isolate these hosts. Rotate these credentials. Check these backups. Not generic playbooks — recommendations derived from the actual detections in your environment.
AI identifies patterns across thousands of nodes that no single rule would catch. Twelve hosts in the same cluster all accessed /etc/shadow within five minutes? Not a coincidence. AI flags it before the attacker reaches their objective.
Because AI-based detection produces false positives. Every ML model has a decision boundary, and adversaries can operate just inside it. We separate the concerns: deterministic engines detect, AI engines analyze. You get certainty where it matters (did this happen?) and intelligence where it helps (what does it mean?).
of security alerts are false positives across the industry (Heimdal 2025). SOC teams spend half their time chasing noise. invariantd reduces that to zero for rule-covered detections.
Instead of describing known attacks (signatures) or learning what's "normal" (anomaly detection), you declare the security properties your infrastructure must maintain. The engine continuously verifies these properties against live system state.
invariant no_shell_from_web { @severity(critical) never (p:Process)-[:spawns]->(c:Process) where p.name in {"nginx", "apache2", "caddy"} and c.path in {"/bin/sh", "/bin/bash"} }
Fires the instant any web server process creates a child shell. No probability. No threshold. Definitive.
invariant escalate_then_tamper { @severity(critical) never { (u:User)-[:escalates]->(u2:User) as step1 then within 60s (p:Process)-[:modifies]->(f:File) as step2 where f.path matches /^\/usr\/s?bin\// } }
Detects multi-step attack sequences. Step 1 alone is not a violation. Step 2 alone is not a violation. The sequence within the time window is the violation.
327 detection rules covering 77% of the MITRE ATT&CK Linux surface. Write custom rules for your environment using the same language.
| Capability | invariantd | CrowdStrike | SentinelOne | Sysdig | Falco | Wiz | Aqua |
|---|---|---|---|---|---|---|---|
| False Positive Rate | Zero by design | Low (AI-tuned) | Low (88% fewer alerts) | Moderate | Moderate-High | Moderate | Moderate |
| Detection Latency | <1ms p99 | Real-time | Real-time | Real-time | Real-time | Real-time | Real-time |
| Detection Method | Formal specification | ML/AI + signatures | Behavioral AI + rules | Rules + ML anomaly | YAML condition rules | Rules + graph correlation | eBPF behavioral + Rego |
| Multi-Step Detection | Temporal sequences | Cloud-side correlation | Storyline (AI) | No | Single-event only | Graph correlation | Single-event only |
| Causal Chain | Deductive proof | Threat Graph (AI) | Storyline (AI) | Alert-level only | Process ancestry | Cloud attack path | Process ancestry |
| Custom Rule Language | Typed temporal IDL | Falcon Query Language | STAR rules | Falco YAML | YAML conditions | Custom runtime rules | Rego + Go |
| Kernel Visibility | eBPF native | eBPF / kernel module | eBPF | eBPF (via Falco) | eBPF / kernel module | eBPF sensor | eBPF (Tracee) |
| Signed Policies | Ed25519 signed | Cloud-managed | Cloud-managed | No | No | Cloud-managed | No |
| Air-Gapped Deploy | Full support | Limited | Announced 2026 | Supported | Self-hosted | Cloud SaaS only | Tracee OSS only |
| Cloud Dependency | None | Required for full function | Required (air-gap coming) | Optional | None | Required | Required |
| CPU Overhead | <3% at 1M ev/s | 1-5% | 1-5% | Low | Low | Low (sensor) | Low (Tracee) |
Purpose-built for ephemeral infrastructure. Protect Kubernetes clusters, container runtimes, and cloud VMs. Detect container escapes, image tampering, unauthorized deployments, and lateral movement across pods and nodes — in environments where workloads spin up and die in seconds.
Full detection coverage for traditional data centers, hybrid architectures, and multi-cloud environments. Single pane of glass across bare metal servers, VMware hosts, and cloud instances. No data leaves your perimeter unless you choose to export it.
Designed for environments with no external network access. Fully self-contained deployment. Signed policies transported via secure media. No telemetry, no phone-home, no cloud dependency. Meets the requirements of classified and SCIF environments.
Lightweight enough for constrained devices. ARM and x86 support. Under 50MB memory. Operates independently when disconnected from the control plane, syncing detections when connectivity resumes. Built for remote sites, retail locations, and industrial edge.
Zero false positive detection is an audit-ready control. Map directly to SOC 2 Type II, PCI-DSS 4.0, NIST 800-53, and FFIEC requirements. Eliminate the "tuning" burden that makes traditional tools a compliance liability. Every detection is deterministic, documented, and defensible.
Protect systems handling PHI and clinical data. HIPAA-aligned detection rules for unauthorized access, data exfiltration, and privilege abuse. Full causal chains provide the forensic evidence required for breach notification assessment — without manual log reconstruction.
Air-gapped deployment with signed, tamper-proof policies. NIST 800-171 and CMMC-aligned detection coverage. FedRAMP-ready architecture. Designed for environments where a false positive triggers an expensive incident response — and a miss is unacceptable.
Protect your production infrastructure and your customers' trust. Detect supply chain compromises, CI/CD pipeline attacks, insider threats, and credential theft. Ship detection rules alongside code — same review process, same deployment pipeline, same rigor.
Reduce mean time to detect from hours to milliseconds. Eliminate alert triage — every detection is actionable. Free your analysts from tuning rules and chasing false positives. Reallocate 90% of tier-1 analyst time to threat hunting and response.
Detection rules map directly to regulatory frameworks. Continuous monitoring satisfies control requirements without manual evidence gathering. Export detection logs as audit artifacts. Replace point-in-time assessments with always-on verification.
When a detection fires, the full attack chain is already reconstructed — which process, which file, which connection, which user, which host, and exactly when. Responders start investigating, not reconstructing. Mean time to understand drops from hours to seconds.
Query the live system model across your entire fleet. Search for behavioral patterns, temporal sequences, and graph relationships. Test hypotheses against real infrastructure state — not stale log archives. Turn hunt findings into permanent detection rules.
Whether you're evaluating runtime detection tools, planning a migration from legacy EDR, or building a security program from scratch — we'd like to hear about it.
AI analysis activates when detections begin
Deterministic detection first. AI analysis second.