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Harden your models before attackers find the cracks.

Model Shield

Tests production ML models against adversarial input attacks, data poisoning, and model extraction attempts, then applies certified defenses to harden models before deployment. Protects banking fraud models, network intrusion detectors, and industrial anomaly classifiers from increasingly common ML-specific attacks.

Why It Matters

ModelShield treats ML models as what they’ve become — attack surfaces.
Fraud models, intrusion detectors, and anomaly classifiers are exactly the systems adversaries most want to fool, and adversarial testing plus certified hardening closes vulnerabilities that traditional security programs don’t even scan for.
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ML-specific attacks are here, not hypothetical

Adversarial inputs that evade fraud models, poisoned training data, and model extraction are documented attack classes — and the models worth attacking are the ones our clients run in production.

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Continuous probing beats point-in-time testing

Models change with every retraining cycle — continuous adversarial testing catches new vulnerabilities that an annual pen test would miss for months.

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Certified defenses, not security theater

Hardening applied before deployment with measurable robustness guarantees — defense that survives scrutiny, not a checkbox.

The Cloudly Advantage

ModelShield opens an entirely new budget for Cloudly — cybersecurity — and gives us a security story for the platform’s highest-stakes products.
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Taps the security budget

CISO spending is larger and more resilient than ML budgets — ModelShield lets BD sell into cybersecurity procurement, a channel the rest of the portfolio doesn't reach.

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Protects our flagship use cases

Fraud models (banking) and intrusion detection (telecom) are both our top vertical stories and the top adversarial targets — security becomes an automatic attach on every such deal.

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Nearly empty competitive field

ML-specific security expertise is scarce globally and almost absent regionally — early positioning makes Cloudly the reference partner as awareness grows.

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Completes the trust story

Fair (BiasDetector), explainable (ExplainabilityLayer), auditable (ModelAuditTrail), and now attack-hardened — the four-pillar responsible-AI offering no regional competitor assembles.

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Continuous probing is recurring revenue

Ongoing adversarial testing across retraining cycles is a managed security service by nature — monthly recurring income per protected model.

The Final Takeaway

AWS WAF services pull-through — WAF ML rules integration ties model defense into broader AWS security architecture — opening cloud security consultancy alongside our Monitoring services.

Powered By

IBM Adversarial Robustness Toolbox

Core to the ModelShield technology stack.

CleverHans

Core to the ModelShield technology stack.

Foolbox

Core to the ModelShield technology stack.

AWS WAF ML rules

Core to the ModelShield technology stack.

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