Use Cases Powered by a Signal-First Architecture

Quick Summary: Two computers are talking normally. When a secret tap is added, the wire’s tiny electrical behavior changes. We watch the raw waveforms and notice small shifts in timing, reflections, and current. This lets us spot a hidden device, even without reading any data. We record the “before and after” signals so teams can prove what happened and respond quickly.

Two ordinary computers are talking. Timing is tight, edges are clean, and the line’s personality—its impedance, delay, and jitter “handwriting”—is well-behaved. Then, mid-conversation, something changes. Our product is already listening at the wire, capturing synchronized voltage–current pairs at extreme speed. It knows this link’s normal physics. The instant a passive or inline sniffer comes online, the wire itself reacts—ever so slightly. A tiny impedance discontinuity appears. Rise-times lengthen by a sliver. Reflections shift phase by a few degrees. Current draw patterns wobble out of their usual rhythm. None of this requires packet payloads. It’s the metal telling the truth about what’s attached to it.

We correlate both sides of the conversation and watch for telltale signatures: reflection coefficients that drift, group delay that bends at a frequency-dependent rate, and edge variance that spikes at consistent intervals. Even a “transparent” tap has a footprint; at the speeds we sample, transparency is a myth. Our anomaly engine compares the live signal against its learned baseline using matched filters, variance tests, and distribution distance measures. If it matches the family of “inline-device” patterns, we elevate. Classification then labels the event: probable sniffer insertion, complete with confidence, timing, and a forensic slice.

Because we store in a way that preserves anomalies (fractal + multi-resolution detail), the exact, authentic waveforms surrounding the moment of insertion are replayable later—down to the edge. That gives operators a courtroom-grade “before/after” view: same cable, same endpoints, different physics. The result is decisive, non-invasive detection that exposes silent observers by their physical footprint alone.

Quick Summary: A remote attacker starts probing your system. We study the timing and behavior of the connection at the wire, not just IPs or logs. From these clues, we can tell real sources from fakes, find likely control points, and act fast. The system can cut bad sessions, slow attacks, or steer them into a safe decoy—while saving clean evidence for follow-up.

An attacker begins probing a protected system from far outside your network—different ASN, different continent. Requests arrive with irregular timing and carefully varied packet sizes. Traditional telemetry sees noise; our system watches the conversation’s physics and the flow’s behavior simultaneously. First, the product validates source reality (handshake integrity, path symmetry, TTL/hop patterns, and timing coherency) to separate spoofed floods from genuine controllable sessions. Then, it fuses multiple hints—SYN/SYN-ACK latency, jitter fingerprints, retransmission cadence, and provider-visible metadata—to triangulate a highly probable source IP and upstream path. If botnets or proxies are involved, their inconsistency shows up in timing and error-control behavior; the system flags the herd while still isolating the likely controller.

Once attribution confidence passes your threshold, the product shifts to active defense. Inline, it can terminate sessions decisively (RST injection), rate-limit or shape flows to collapse the attacker’s ROI, and steer malicious traffic into a deception enclave where interaction wastes their time but never touches production. It can signal upstream for black-hole or RTBH actions, push indicators to your blocklists, or program hardware filters at line-rate to stop the specific behaviors observed (not just IPs). Throughout, the physics-layer capture preserves the exact evidence of the attack—timing, retries, anomalies—so you can prove what happened, when, and from where.

The defender’s edge is speed and certainty: detect at the wire, attribute with multi-factor evidence, and counter-strike defensively within policy—closing sessions, isolating targets, and null-routing hostile paths—while keeping a verifiable trail for legal or diplomatic follow-through. Even when the adversary is a hemisphere away, the local physics and your network controls make the distance irrelevant.

Quick Summary: Some images are taken by real cameras; others are made by AI. We can tell the difference while the file is moving over the network. By reading the timing and size patterns of the transfer—and, when allowed, running fast forensic checks—we spot signs of AI generation. This works even when traffic is encrypted, and we save proof for policy action.

A photo is on the move—captured, encoded, and sent. Our Watcher product sits at the wire, watching the physics of the transfer and, when permitted, the structure of the content. A real image produced by a sensor carries a distinct lineage: lens optics, sensor photo-sites, demosaic math, compression tables, and device firmware all leave measurable fingerprints. An AI-generated image is born differently: a neural generator renders textures from latent codes, then a software pipeline packs pixels without sensor physics. Even when payloads are encrypted, these two lineages create different transmission behaviors—and we learn those differences at speed.

Physics-layer (no payload required). We profile TLS/QUIC record sizes and inter-arrival jitter across the transfer and compare them to learned priors. Real camera JPEGs/HEICs exhibit characteristic marker, quantization, and restart segment rhythms that survive through encrypted record-size histograms and burst timing. Generator pipelines often emit buffers with different chunking and cache-flush cadence. We transform the length/time series with multi-scale wavelets, compute divergence metrics (e.g., KL, Wasserstein), and test with a Neyman–Pearson detector tuned for low false-positives. On endpoints where we have rail visibility, the act of generating an image produces distinctive sub-millisecond GPU/CPU power signatures—short tensor bursts followed by encode—and we correlate those V–I micro-events with the immediate egress. The result is a real-time “this looks like AI-made” decision that does not depend on reading pixels.

Content-aware (when plaintext or a sanctioned mirror is available). We run fast forensic checks: sensor PRNU (photo-response non-uniformity) consistency, demosaic periodicity, lens-shading and rolling-shutter traces, double-compression and resampling artifacts, and JPEG quantization footprints. We fuse these with learned generative cues (phase statistics, patch self-similarity, over-regular textures). If invisible watermarks are present, we verify them; if not, we rely on the physics+forensics ensemble. Throughout, our anomaly-preserving storage retains the exact waveforms and bytes (or their provable transforms), enabling courtroom-grade replay of how we knew—down to edge timings on the wire.

Quick Summary: Stealth drones and aircraft try to fool radar with special shapes, coatings, clever flight paths, and quiet radios. We watch the raw electrical waveforms at the sensors themselves. Tiny ripples, timing shifts, micro-Doppler from props or rotors, and other “hidden” patterns still show up in the signal. That’s how Watcher can still see what no one else can see and still hear what no one else can hear — even when screens look empty.

Adversaries lower radar cross-section with faceted shaping and RAM coatings, fly terrain-masked routes, use LPI/LPD waveforms, hop carriers, and keep radios silent. They spoof, decoy, and split returns across bearings to confuse trackers. Traditional displays may show ghost blips or nothing at all. Watcher instruments the receiver chain and associated power/RF paths directly—at the electrical layer—sampling baseband/IF waveforms at extreme speed and correlating them with front-end behavior (AGC nudges, mixer leakage, LO pulling, rail transients).

Even when the plotted return is faint, the physics leaks clues: sub-harmonic sidebands from rotor blades (micro-Doppler), slow phase wander from RAM-induced scattering, repeatable envelope “breathing” from LPI chirp responses, and noise-floor dimples where adaptive filters work a bit too hard. We fuse multi-site timing, passive illuminators of opportunity, and signal-plus-sensor side-channels (minute current draws, clock jitter coupling) to raise confidence. With matched filters and multi-resolution time–frequency views, Watcher isolates the pattern family that points to a stealth unmanned aircraft—not just “a target,” but a target with specific flight physics.

Because our capture preserves anomaly detail, operators can replay the exact waveforms around each cue—micro-Doppler ridges, phase flicker, and AGC micro-steps—and compare them to learned baselines. In short: Watcher can still see what no one else can see and can still hear what no one else can hear, and—by following the physics—can still hear what no one else can see and can still see what no one else can hear.