Aether for application security.
The whole AppSec stack, on one model.
SAST, SCA, DAST, IAST, RASP, secret scanning, IaC, API security, supply chain and ASPM — the tools you buy from five vendors, in one platform with one finding model, one prioritised queue and an agentic copilot that explains, triages and fixes. Incremental PR scans in under five minutes, false positives under ten percent, deployable self-hosted and air-gapped.
- Engines
- 12, one platform
- Coverage
- IDE → runtime
- PR feedback
- ≤ 5 min
- Deploy
- Cloud · self-hosted · air-gapped
Twelve engines, one finding model.
The incumbent AppSec stack is five products from five vendors with five consoles, and the seams between them are where vulnerabilities slip through. Aether runs every engine on one platform — they share a finding model (SARIF), a correlation engine and a single prioritised queue, so a flaw seen statically, confirmed at runtime and reachable in production is one finding, not three tickets nobody connects.
Static analysis (SAST)
Multi-language dataflow and taint analysis with custom query authoring, OWASP/CWE mapping and monorepo scale — incremental scans for PR feedback, full scans for the whole estate.
Software composition (SCA)
Dependency vulnerability scanning with a provenance graph, reachability analysis, license compliance, curated intelligence and malicious-package detection — with automated upgrade PRs.
Dynamic analysis (DAST)
Black-box and authenticated dynamic testing, API DAST, crawl and attack-surface mapping, run in CI against a live build.
Interactive analysis (IAST)
Runtime instrumentation that confirms reachability and exploitability from inside the running app — runtime SCA and low false positives from real execution context.
Runtime protection (RASP / ADR)
Application self-protection and detection-and-response in production, with runtime virtual patching and attack telemetry — under a 5ms P95 latency budget.
Secret scanning
Detection across code and full history, push protection at commit time, validity checking with revocation guidance, and custom secret patterns.
IaC & container config
Infrastructure-as-code and Dockerfile misconfiguration scanning, cloud config policy and pre-deploy gating — before a bad config ships.
API security
API discovery, security testing, spec conformance, shadow- and zombie-API detection, and runtime API protection.
Software supply chain
SBOM generation, SLSA provenance and attestation, dependency-integrity verification, pipeline-security posture and malicious-dependency protection.
Remediation engine
Auto-fix pull requests, AI-assisted code remediation, guided workflows, fix verification, and bulk remediation campaigns across the fleet.
SDLC integration
IDE, CLI, SCM PR checks and CI/CD gating with scan orchestration and developer feedback in the pull request, where the fix actually happens.
Posture management (ASPM)
Unified findings aggregation, reachability-weighted prioritization, cross-engine correlation, risk scoring with business context, SLA tracking and automated triage.
The same finding, across the lifecycle.
Security that meets developers where they work — and follows the code all the way to production, instead of stopping at the scan.
Real-time SAST and secret detection as code is written, with secure-coding guidance in the flow — feedback in under 2 seconds.
Incremental SAST, SCA and IaC scans post results as PR checks in under 5 minutes, with auto-fix PRs and the rationale inline.
Full scans, DAST against the build, SBOM and provenance attestation, and risk-based release gating with a configurable failure mode.
IAST confirms what's actually exploitable, RASP self-protects and virtually patches, and runtime telemetry closes the loop back to the finding.
The whole stack, one model.
The incumbent program is a separate product for each box, and the gaps between them are the risk. Aether covers the whole surface on one model, one finding queue and one audit trail — and runs where your code is allowed to live.
An AppSec engineer, not a scanner.
The model isn't a chatbot bolted on the side — it reduces the false positives, infers reachability, generates and verifies the fixes, and triages the backlog, with every decision gated by authorization tiers and logged for audit.
AppSec copilot
A natural-language assistant over your whole security posture — explain a vulnerability, author a policy or a custom query, triage a backlog, in plain language.
Agentic triage
Automated prioritization and noise reduction across engines, surfacing the findings that are reachable and business-critical, not just the highest CVSS.
AI auto-remediation
Generated fixes that are verified against the finding before they're proposed — and gated by authorization tiers so the agent acts only within an explicit envelope.
False-positive reduction
A model that learns which SAST findings are real, holding the false-positive rate under 10% so developers trust the queue instead of ignoring it.
Reachability inference
Inferred exploitability and reachability that weight prioritization, so an unreachable 'critical' drops below a reachable 'medium'.
Audited AI decisions
Every AI action carries its confidence and rationale, and every decision is logged — so an auditor can see why the agent did what it did.
Fast enough to act on.
Security tools get ignored when they're slow or noisy. These are the engineering targets the platform is built to — measured, not aspirational.
PR feedback on a change
across ≥ 1M lines of code
as you type
runtime instrumentation
added in production
a queue you can trust
estate scale
self-hosted or air-gapped
One platform for the whole program.
Five vendors, five consoles and a pile of integration glue collapse into one model, one finding queue and one audit trail.
Good to know.
Those are separate products with separate findings, separate consoles and separate licences — and the gaps between them are where risk hides. Aether is one platform: SAST, SCA, DAST, IAST, RASP, secrets, IaC, API and supply chain feed one finding model (SARIF), one correlation engine and one prioritised queue, so a vulnerability seen statically, confirmed at runtime and reachable in production is one finding, not three.
Because the false-positive rate is held under 10% by a model trained to recognise real findings, and reachability inference drops the unreachable ones below the reachable ones. Findings arrive in the PR with the rationale and an auto-fix, in under five minutes — fast and accurate enough to act on rather than ignore.
Auto-remediation is gated by authorization tiers — the agent proposes and, within an explicit envelope, can open a fix PR, but it can't act outside what you've granted. Every fix is verified against the finding before it's proposed, and every AI decision carries its confidence and rationale and is logged for audit.
Yes — self-hosted and fully air-gapped, with ephemeral code handling, data residency, encryption and tenant isolation. Your source and secrets are protected; findings egress as SARIF to your SIEM, and the platform never needs your code to leave the boundary.
Replace the AppSec stack with one model.
SAST to RASP, IDE to production, on one platform with one prioritised queue and an agentic copilot — under five-minute PR feedback and a sub-10% false-positive rate. Request access to deploy it self-hosted or air-gapped behind your firewall.
Part of Aether Security — one platform across thirteen domains. Application security sits on the same foundation as the rest of Aether — one model, every discipline.