ADA

Securing your future
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Securing made simple

Real scans,
not guesswork.

Security for the era of vibe coding.

ADA scans your code and detects vulnerabilities using semgrep and a dedicated secret scanner, catching real issues like SQL injection, command injection, and leaked API keys.

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We're at a tipping point for how code gets shipped.

Our mission: Securing your future.

ADA is an accuracy-first, AI-native vulnerability platform for the vibe-coding era. Developer-first. Trust-obsessed.

People don't trust scanners — too many false alarms, or worse, missed real bugs. So we obsess over one thing: accuracy. Not finding counts. Not flashy dashboards. Just what's real.

Why this matters

When developers engage in vibe coding—using AI to rapidly generate features via natural language without worrying about syntactic details—traditional security testing breaks down. Generative models are trained to prioritize "making the code work on the happy path." As a result, research shows that 40% to 60% of unguided AI-generated code contains security flaws like unparameterized SQL, missing auth checks, or insecure deserialization (pickle.loads()).

Measured, not claimed

92.4%
Detection rate

Measured against 1,526 annotated vulnerable lines across 429 files — a corpus written by Semgrep's own team, never seen while ADA's rules were authored.

Our custom ruleset beats Semgrep's flagship security pack head-to-head — 29.8% versus 25.2% on the same corpus, rule-for-rule. Run together they reach 92.4%, because they catch different vulnerabilities: ours target the idioms that show up in AI-generated code and nowhere else. Every number is reproducible from the benchmark harness in our repo, caveats included.

Values

Accuracy Over Hype

We don't chase headline vulnerability counts. A finding that isn't real isn't a feature — it's noise. Signal over volume, always.

Deterministic by Design

Every finding traces back to a rule you can audit — detection is 100% semgrep, trufflehog and OSV. AI reads findings afterwards to judge and explain them; it can question a finding, never invent or hide one.

Built for Vibe Coding

Shipping fast with AI-generated code isn't going away, and it shouldn't have to. ADA fits into that workflow instead of asking you to slow down for a security review you'd skip anyway.

Builders, Not Just Scanners

We're shipping alongside the people who use this daily. Fast iteration, real feedback loops, zero ivory tower.

Security by Default

Safe should be the easy path, not the extra step. We're building toward guardrails that fit into how code already gets written.

No Security Background Required

You shouldn't need a security degree to ship safely — that's the whole point. Built for builders using Lovable, Bolt, Cursor, and everything in between.

Our current focus

Ada Reasoning Engine AdaRE

The moat isn't detection — Snyk, Semgrep, and CodeQL already do that well. It's understanding. AdaRE is the reasoning layer that sits on top of every scanner: it re-reads each finding in context, separates real threats from false alarms, explains the impact in plain English, and generates a fix you can actually trust — the judgment a security engineer adds, on top of scanners we integrate rather than try to replace. It already powers the triage, explanations, and suggested fixes you see in the scanner today.

Aggregate scanners Understand context Triage real vs. noise Model the threat Generate a trusted fix

What's coming next in the ADA ecosystem

IDE Extension Coming soon

Scan as you code, right inside your editor — catch issues the moment they're written, before they ever reach a commit.

Cofixer Coming soon

Don't just flag what's wrong — get the safer fix, ready to apply. Remediation, not just detection.

ADA Intelligence Planned

Threat modelling and attack simulation on top of findings — show the actual exploit path, not just the flagged line. Aggregating more scanners as we go, since detection is a commodity and judgment isn't.

Our backgrounds

Our backgrounds span offensive security and applied AI, and ADA is built at that intersection.

Detection — the ruleset, the benchmark harness, the security model behind the app itself — comes from graduate cybersecurity work at Georgia Tech and hands-on offensive security: web application attacks, binary exploitation, intrusion detection, and exploiting Log4Shell in controlled environments.

AI triage comes from Stanford CS and production engineering on an AI-powered security platform — LLM agents, retrieval pipelines, and vulnerability management workflows.

Finding vulnerabilities and judging which ones actually matter are different problems. We build them as different systems, and we publish the numbers for both.

Founder
  • Georgia Institute of Technology — M.S. Cybersecurity, Information Security
  • San José State University — B.S. Information Science & Data Analytics, Dean's Scholar 2023 & 2024
  • Offensive security coursework: web attacks and defenses (XSS, CSRF, session hijacking, CSP), binary exploitation (buffer overflows, ROP), Log4Shell (CVE-2021-44228)
  • Custom Snort NIDS rules for botnet C&C detection; ML classification of malicious network traffic

Want to get in touch, or see the full team's LinkedIn profiles and backgrounds? Send us a message.

Have a question, feedback, or a partnership idea?

Get in touch.

Send us a message and we'll get back to you — or email us directly at contactus@adasecure.net.