Three native Rust AI agents detect harvest-now-decrypt-later attacks, side-channel exploits, and cryptographic degradation. Sub-microsecond latency. Zero external dependencies. No Python. No ML frameworks.
Detects "harvest now, decrypt later" quantum attacks. Monitors for bulk ciphertext collection patterns that indicate adversaries stockpiling encrypted data for future quantum decryption.
Detects timing, power, and cache side-channel attacks in real-time. Monitors cryptographic operation patterns for statistical anomalies that indicate active side-channel exploitation.
Continuous assessment of cryptographic parameter health. Alerts on weakening algorithms, approaching key rotation deadlines, and parameter degradation before they become vulnerabilities.
Incoming API calls and encrypted data streams enter the detection pipeline
All three agents analyze in parallel: harvest, side-channel, and crypto health
Composite threat score computed from all agent signals in under 1.2µs
Configurable response: block, alert, log, or escalate based on thresholds
curl -X POST https://api.h33.ai/api/ai/harvest-detect \
-H "Authorization: Bearer $H33_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"traffic_sample": "base64_packet_data...",
"window_seconds": 60,
"source_ip": "203.0.113.42"
}'// Response
{
"threat_detected": true,
"threat_type": "harvest_bulk_collection",
"confidence": 0.94,
"latency_us": 0.69,
"action": "alert",
"indicators": ["volume_spike", "key_material_targeting"]
}curl -X POST https://api.h33.ai/api/ai/side-channel \
-H "Authorization: Bearer $H33_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"operation_timings_ns": [1200, 1195, 1210, 1198, 1400, 1205],
"operation_type": "aes_encrypt",
"sample_count": 6
}'// Response
{
"anomaly_detected": true,
"attack_type": "timing_attack",
"anomaly_index": 4,
"confidence": 0.87,
"latency_us": 1.14,
"severity": "high"
}curl -X POST https://api.h33.ai/api/ai/crypto-health \
-H "Authorization: Bearer $H33_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"algorithms": ["RSA-2048", "AES-256-GCM", "SHA-256"],
"key_ages_days": [180, 45, 90],
"rotation_policy_days": 90
}'// Response
{
"overall_health": "warning",
"score": 72,
"latency_us": 0.52,
"alerts": [
{
"algorithm": "RSA-2048",
"issue": "approaching_rotation",
"days_remaining": 0,
"action": "rotate_now"
},
{
"algorithm": "RSA-2048",
"issue": "quantum_vulnerable",
"recommendation": "migrate_to_dilithium"
}
]
}All three agents complete in under 1.2 microseconds combined. Harvest detection at 0.69µs, side-channel at 1.14µs, crypto health at 0.52µs. No perceptible overhead.
No Python interpreters. No TensorFlow. No ONNX runtime. No scikit-learn. Pure compiled Rust binaries with zero garbage collection pauses.
No ML model downloads. No GPU requirements. No container sidecars. Each agent is a self-contained Rust module that compiles into your H33 deployment.
Agents analyze traffic as it flows. No batch windows. No delayed alerts. Threats are detected and scored within the same API call that triggered them.
Set custom threat score thresholds for block, alert, and log actions. Tune sensitivity per agent. Override defaults per endpoint or per tenant.
Every detection event is logged with full context: threat type, confidence score, source metadata, and action taken. SOC 2 and HIPAA audit trail compatible.
Detect harvest attacks targeting financial transaction data. Real-time side-channel monitoring for trading systems. Continuous crypto health assessment for payment infrastructure and regulatory compliance.
Protect classified-adjacent communications from quantum harvest campaigns. Monitor for state-sponsored side-channel attacks. Ensure all cryptographic parameters meet NIST post-quantum standards.
Safeguard patient health records from long-term harvest attacks. Detect side-channel exploits against medical device encryption. Maintain HIPAA-compliant cryptographic posture with continuous health monitoring.
Deploy across your entire API surface with zero latency overhead. Protect intellectual property from quantum-era threats. Automated alerting integrates with existing SIEM and incident response workflows.
| Feature | H33 AI Detection | CrowdStrike Falcon | Darktrace | Traditional IDS |
|---|---|---|---|---|
| Detection latency | <1.2 µs | ~100ms | ~500ms | Seconds-minutes |
| Architecture | Native Rust (compiled) | Cloud agent + cloud ML | Cloud appliance | Signature-based |
| Quantum threat detection | Yes (harvest detection) | No | No | No |
| External dependencies | 0 (zero) | Cloud API required | Cloud required | Signature DB |
| Side-channel detection | Real-time (1.14 µs) | Not available | Limited | Not available |
| Crypto health monitoring | Continuous (0.52 µs) | Not available | Not available | Not available |
| Deployment model | Embedded (in-process) | Agent + cloud | Network appliance | Inline/TAP |
| ML framework | None (pure Rust logic) | Python/TensorFlow | Proprietary ML | Regex/YARA |
0.69µs. This is the single most critical quantum-era threat vector, and most organizations have zero visibility into it today.<0.1% false positive rate on production traffic. You can adjust sensitivity per endpoint or per tenant using the threshold configuration in each agent's API call./api/ai/harvest-detect, /api/ai/side-channel, and /api/ai/crypto-health. Call only the agents you need. They are independent modules with no cross-dependencies./api/ai/side-channel endpoint. The agent performs statistical analysis to detect timing variance anomalies that indicate cache, power, or electromagnetic side-channel exploitation. The analysis completes in 1.14µs and returns the anomaly index, attack type classification, and severity assessment.block (reject the request), alert (notify via webhook), log (record for audit), or escalate (trigger incident response). Actions are set in the agent configuration and can be overridden per-request via the API.H33-Key, H33-Shield, or custom) for automated remediation. The agent tracks key_ages_days against your rotation_policy_days and flags quantum-vulnerable algorithms like RSA for migration to post-quantum alternatives.2.5 µs combined for all three agents in sequence (0.69 + 1.14 + 0.52 = 2.35µs). This is typically less than 0.01% of a typical API request latency. No perceptible impact on throughput. When run in parallel, the wall-clock overhead is limited to the slowest agent at 1.14µs.Get your API key in seconds. Three AI agents protecting your infrastructure at microsecond speed.
Deploy AI Detection3 agents · <1.2µs max latency · Zero external dependencies