Protect AI
Protect AI is an enterprise AI security platform that combines model scanning, scalable AI red teaming, and runtime threat detection to help organizations assess and mitigate risks across model formats and AI application types including RAG systems and agents.
Overview
Protect AI positions its products as a unified platform for AI security across the AI lifecycle, from model selection and testing through runtime. The official site groups the offering into Guardian, Recon, and Layer. Guardian is presented as model security scanning across major model formats with detection for issues such as deserialization threats, architectural backdoors, and runtime risks.
Recon is positioned as scalable red teaming and vulnerability assessment that provides actionable insights for model selection and evaluating defenses before production. Layer is presented as runtime security scanners designed to detect attack patterns and protect AI applications including RAG systems and agents while integrating with custom scanners. Pricing is not published as a self serve checkout on the official site and the primary call to action is requesting a demo, so procurement is best treated as enterprise by quote.
For evaluation, focus on coverage for your model formats and deployment stack, integration points into CI and runtime, reporting needs for governance, and how findings map to remediation workflows owned by security and ML teams.
Key features
- Guardian scanning: Scan models for security issues across major model formats with checks targeting threats like backdoors and unsafe deserialization
- Recon red teaming: Run scalable AI red teaming and vulnerability assessments to surface risks before launching AI apps to production
- Layer runtime detection: Use runtime scanners to detect attack patterns and protect AI apps including RAG systems and agents in production
- Unified platform: Operate Guardian Recon and Layer within one platform to align findings and workflows across teams
- Integration emphasis: Product pages highlight integration with existing scanners and environments to fit into current security programs
- Pre production decisions: Use Recon insights for model selection and evaluating the effectiveness of existing defenses
Best for
- Model intake review: Scan third party models before deployment to catch unsafe formats and known threat patterns early
- Pre launch testing: Red team an AI app to identify prompt injection and misuse risks then prioritize mitigations before go live
- Runtime monitoring: Detect hostile prompts or suspicious behavior patterns in production AI systems including RAG and agent flows
- CI security gates: Add model scanning into build pipelines so releases fail when risk thresholds are exceeded
- Vendor governance: Evaluate model providers with consistent scanning and test reports for procurement and audit
- Incident response: Use findings and logs to triage suspected AI attacks and coordinate remediation across ML and security teams
Capabilities
Model scanning
Guardian focuses on scanning models across major formats and identifying threat classes like unsafe deserialization and backdoors, helping teams gate model intake and reduce supply chain risk before deployment.
AI red teaming
Recon supports scalable red teaming and vulnerability assessments that inform model selection and defense evaluation, enabling pre production risk discovery and clearer remediation priorities.
Runtime detection
Layer targets runtime detection for AI applications including RAG systems and agents, identifying attack patterns and integrating with existing scanners to support defense in depth in production.
Security operations fit
Adopt the platform through CI and runtime integration points, then map findings into ticketing and governance workflows so owners can remediate issues and track risk posture over time.
Frequently Asked Questions
How does Protect AI pricing start?
AI security tooling touches regulated data and customer content. Review contract terms, data processing, retention, and audit expectations, and ensure your internal policies cover testing in production like environments.
Does Protect AI integrate with CI or existing tooling?
Product pages emphasize integration and compatibility with custom scanners. Validate integration points for your CI pipeline and runtime stack, confirm reporting formats, and test how findings flow into your remediation process.
What setup and skills are required to use it well?
You will need security and ML owners to define risk thresholds, choose model formats and environments to scan, and tune workflows for triage. Start with a pilot on a limited app and expand once results are actionable.
How does it compare to generic application security tools?
Generic appsec tools rarely cover model specific threats and AI red teaming depth. Protect AI is positioned for AI lifecycle security across scanning, red teaming, and runtime detection, so compare based on AI coverage and operational fit.
