AI Threat Guides

Comprehensive guide to protecting organizations against AI threats — combining a 7-step strategic framework with 10 technical security best practices for LLM applications. Covers threat surface mapping, governance, technical hardening, agentic AI security, red teaming, monitoring, and incident response.

A 4-step methodology for detecting AI bias and assessing fairness in AI systems—covering data audits, fairness criterion selection, disparate impact testing, and production monitoring. Includes tools comparison and the fairness impossibility theorem.

A 5-phase AI incident response framework covering detection, containment, investigation, remediation, and regulatory reporting—including EU AI Act Article 62 obligations and AIID submission guidance.

Step-by-step workflow for identifying adversarial inputs targeting AI systems, including input validation, transformation testing, behavioral monitoring, and response procedures for security and ML teams.

Step-by-step workflow for auditing AI systems for discriminatory outcomes, including fairness metric selection, disaggregated evaluation, data auditing, and regulatory compliance guidance.

Step-by-step workflow for identifying AI-generated phishing emails and messages. Quick-reference checklists for email authentication, behavioral indicators, automated analysis, and organizational response.

6-step workflow to detect AI-generated text. Includes manual indicators, Python code for stylometric analysis, detection tool comparison, and decision framework.

Step-by-step workflow for identifying and responding to data poisoning attacks on AI training data, fine-tuning corpora, and RAG knowledge bases. Covers pre-training inspection, during-training monitoring, post-deployment detection, and remediation.

Step-by-step workflow for evaluating suspected deepfake video, audio, or images. Quick-reference checklists for visual inspection, audio analysis, provenance verification, and escalation guidance.

Step-by-step workflow for evaluating suspected AI-cloned voice audio. Quick-reference checklists for audio analysis, prosodic inspection, automated detection, out-of-band verification, and escalation guidance.

Prevent prompt injection in LLM apps with 6 layered defenses. Includes code examples, implementation checklist, OWASP mapping, and multi-tenant guidance.

AI red teaming is the adversarial evaluation of LLMs and agentic AI systems before deployment, testing for jailbreaks, prompt injection, harmful outputs, and bias. A 4-phase methodology with tools comparison.

Step-by-step workflow for securing AI model supply chains, including model provenance verification, dependency scanning, data source authentication, third-party tool security, and ongoing supply chain monitoring.

A phased checklist for safe AI deployment covering security configuration, governance sign-off, testing gates, and post-deployment monitoring. Tied to real incident types from the topaithreats database and EU AI Act Article 9 requirements.