Enterprise AI Security Field Guide
A practical guide to testing, validating, and securing enterprise AI systems.
Understand how risk emerges across models, applications, agents, data, tools, integrations, and business workflows, and how manual adversarial testing can identify weaknesses before attackers do.
Download the Free GuideIndependently validated security and compliance programs supporting enterprise security engagements.
What You’ll Learn
A practical framework for understanding, testing, and reducing risk across enterprise AI systems.
Enterprise AI Attack Surface
Understand where AI risk exists across models, data, applications, integrations, identities, and workflows.
Explore the ecosystemAgentic AI Security
Learn how autonomous agents create new attack paths through tools, permissions, memory, and automated actions.
Understand agent riskAdversarial AI Testing
See how attackers manipulate trust, chain weaknesses, and turn isolated findings into measurable business impact.
Think like an adversaryAI Security Reporting
Learn how validated findings are translated into evidence, business context, remediation priorities, and measurable risk reduction.
Turn findings into actionEnterprise AI Programs
Build a repeatable operating model for discovery, validation, remediation, reassessment, and continuous assurance.
Build the programFrameworks & References
Reference established guidance from NIST, OWASP, MITRE, ISO, CISA, ENISA, and leading AI technology providers.
Review the standardsExplore the Guide
Four focused chapters take readers from understanding the enterprise AI attack surface to building a repeatable security program.
Download the Enterprise AI Security Field Guide
Gain practical guidance for identifying, testing, and reducing risk across enterprise AI systems, agentic workflows, LLM-powered applications, and modern AI attack surfaces.
30-page executive guide
Enterprise AI testing methodology
Agentic AI security guidance
Real-world attack scenarios
OWASP • MITRE • NIST references
Immediate PDF download
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More AI Security Research
Explore additional Redbot Security research on enterprise AI risk, agentic systems, adversarial testing, and emerging attack techniques.
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How coordinated autonomous agents compress attack timelines, adapt in parallel, and reshape the next generation of offensive security risk.
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Defending In The Mythos Era
Why AI security must move beyond the model to test agents, tools, RAG pipelines, identity, APIs, approval paths, and real attack chains.
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AI Cyber Threats Are Becoming Operational
How Claude Code misuse, AI-powered penetration testing tools, fake developer repositories, exposed secrets, and autonomous agents are changing modern attack paths.
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