The platform is three things working in concert: an inference engine trained on twelve years of attack telemetry, a four-layer defense architecture deployed at the edge and at the host, and a control plane your engineers will actually want to use.
Sentinel Core is an ensemble of three specialist models — signature, statistical, and intent — coordinated by a meta-classifier that arbitrates disagreement. Trained on 4.2 petabytes of labeled attack telemetry and retrained continuously against live traffic, it makes a decision on every request before the request finishes arriving.
A transformer trained on 18 million attack signatures across CVE-mapped families. Catches the known. Not pattern-matching — semantic understanding of attack shape, so it generalizes to mutations that defeat regex-based WAFs.
An unsupervised model that learns each customer's normal traffic distribution within 72 hours of deployment, then flags outliers. Catches the never-before-seen by noticing it's not the always-before-seen.
A behavioral model that scores the sequence of actions, not just the action itself. A login is not suspicious. A login followed by twenty failed logins followed by a password reset request is the start of an account takeover.
When the three models agree, action is automatic. When they disagree, the arbiter weighs the asset's value, the attack's blast radius, and the cost of a false positive — then decides. Human review is reserved for the rare 0.4% that arbitration cannot resolve.
Most defenses fail because they sit in one place. RiseEagle deploys at four — each catching what the layers above missed, each feeding the layers below richer context.
Every event is evaluated through five complementary methods. The percentage shown is the share of confirmed threats each method was first to catch in 2025 — they overlap by design.
Known attack families, CVE-mapped exploits, and toolkit fingerprints. Fast, precise, and the floor — every modern defense has this.
Catches what signatures miss: zero-days, custom exploits, and slow-burn attacks. Learns what your traffic looks like, alerts when reality diverges from that.
Looks at the trajectory of a session, not just the request. The action that triggers the alert is rarely the action that gave the attacker away.
External signal: leaked credential databases, dark-web chatter, exposed-asset scans, and intelligence shared across the RiseEagle fleet.
Honeypot endpoints, decoy credentials, and tarpitted responses. Small in volume, high in signal — a request to a deception endpoint is, by construction, never legitimate. Catches the patient, surgical attacker that everything else misses.
Detection without response is theater. Sentinel Core selects from a graded set of actions — proportionate to confidence, asset value, and the cost of being wrong.
Low-friction proof-of-work or invisible browser check. Used for ambiguous signals where the cost of blocking a real user exceeds the cost of letting a bad one through.
HTTP refusal with a clean error. The request never reaches your origin. Logged, attributed, and contributed to the model's continuing education.
TCP reset, ASN-wide soft block, and propagation across the fleet. Reserved for high-confidence malicious sources — botnets, known toolkits, sustained attackers.
For runtime compromise: pause the affected container, snapshot for forensics, route traffic to healthy replicas, and roll back if state was mutated.
We're often compared to traditional WAF and CDN-based protection. The honest comparison is below. Where they match us, we say so.
| Capability | Legacy WAF | CDN bundles | RiseEagle |
|---|---|---|---|
| OWASP Top 10 — known signatures | ✓ full | ✓ full | ✓ full |
| Zero-day & unknown exploit detection | — signature lag | ~ limited heuristics | ✓ behavioral |
| API schema enforcement (REST/GraphQL/gRPC) | — partial / addon | ~ REST only | ✓ native |
| Sophisticated bot mitigation | ~ rate limits | ✓ headless detection | ✓ behavioral fingerprint |
| Runtime / kernel-level visibility | — none | — none | ✓ eBPF sensors |
| Account takeover & identity defense | — external addon | ~ basic | ✓ integrated |
| Cross-tenant attack propagation defense | — none | ~ partial | ✓ fleet learning |
| False-positive rate on production traffic | — often > 1% | ~ 0.3–0.8% | < 0.03% |
| Median decision latency | ~ 80–200ms | ~ 25–60ms | 38ms |