Robusto by Cortexa Labs

AI Security Trust Infrastructure. MLSecOps for production AI.

Model Security for production AI.

Modeled exposure of $150K+ a year in adversarial incidents, audit prep, and model rework. We exist to remove it.

MLSecOps without the headcount.

Pre-production AI red teaming and model hardening.

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Value Propositions

Fewer Retraining Cycles

Patch known weaknesses with targeted synthetic data instead of repeated full retraining loops.

Pre-Production Red Teaming

Stress-test adversarial and edge-case behavior before release so teams can harden models with intent.

More Time Back

Shorten iteration timelines so teams ship safer, stronger models faster with more release confidence.

Backed By and Built With

Fusen, Google Cloud for Startups, Georgia Tech

Customer ROI (Modeled)

A typical Cybersecurity ML team running 10 models in production spends roughly $570K per year on adversarial-incident exposure, manual robustness testing, and compliance audit labor. Robusto reduces that by approximately $368K per year.

ICP	Modeled annual savings	Saved per dollar (modeled)
Cybersecurity ML	$368K	6.1x to 61x
FinTech ML	$548K	9.1x to 91x
Healthcare ML	$461K	7.7x to 77x

Modeled customer savings, based on third-party industry benchmarks (IBM Cost of a Data Breach 2025, EU AI Act Articles 15 and 99 via Eur-Lex, HIPAA enforcement penalties via HHS OCR). Regulatory exposure applies to high-risk AI systems as defined under EU AI Act Annex III.

How an Engagement Works

Robusto engagements are scoped to your ML stack and regulatory regime. Three phases, run by the Cortexa Labs team.

Step 1 — Scope your models

Walk through the ML models you have in production, the regulatory regime they fall under, and your current robustness posture.

Step 2 — Adversarial diagnostic

Run a 2-week diagnostic across attack types relevant to your domain. Output: a ranked list of vulnerabilities and a robustness baseline.

Step 3 — Hardening and reporting

Apply selective evolutionary training to harden weak models. Deliver a compliance-shaped report mapped to EU AI Act and NIST AI RMF.

Capabilities

• Research-Led: Every defense primitive maps to a measurable threat model.
• Policy-Aware: Safety, compliance, and abuse posture in one scorecard.
• API-First: Use CI hooks and runtime checks with minimal integration work.

What Robusto Is Designed To Save (Modeled)

Outcome	Detail
Up to $168K a year of manual robustness testing labor removed	Automated attack simulation replaces 70% of manual robustness testing labor across 10 models.
Up to $180K a year of modeled adversarial-incident exposure removed	Proactive hardening cuts modeled production incidents by 60%, against a typical 2-incident-per-year baseline.
Around $20K a year of EU AI Act and NIST audit prep removed	Auto-generated EU AI Act and NIST AI RMF evidence cuts compliance prep from 200+ hours to roughly 60.
5 lines of code vs 4 to 8 weeks of ML engineering	Integrate without building a custom reliability pipeline. Engineering time goes back to product work.
Up to €15M or 3% of global turnover in EU AI Act exposure averted	EU AI Act Article 15(5) explicitly names adversarial examples and model evasion. Article 99(3) sets the penalty for high-risk system non-compliance under Annex III (credit decisioning, healthcare ML, employment screening). Eur-Lex