40 in-depth, practitioner-written guides on securing the AI layer — from prompt injection and AI governance to post-quantum encryption and deepfake fraud. Written and reviewed by the Deflected Security Team.
How to secure AI agents: managing excessive agency, tool-use risk, and prompt injection in agentic AI. A practical security model for LLM agents from Deflected.
Read the guide → AI SecurityAI data leakage explained: how sensitive information escapes through LLM output, RAG, logs, and providers — and how to build defense-in-depth against exfiltration.
Read the guide → AI SecurityA practical AI incident response guide: why AI incidents differ, how to adapt the NIST SP 800-61 lifecycle, detection signals, containment, forensics, and comms.
Read the guide → AI SecurityAI red teaming vs penetration testing: how they differ in scope, objectives, methodology, cadence, and outputs — and why AI systems need continuous adversarial testing.
Read the guide → AI SecurityAI red teaming is adversarial testing of AI systems. Learn what it is, how it differs from pen testing, the technique spectrum, frameworks, metrics, and how to build a program.
Read the guide → AI SecurityAdversarial machine learning attacks manipulate AI models through evasion, poisoning, extraction, and prompt-level inputs. Learn the threats and the defenses.
Read the guide → AI SecurityA practical guide to deepfake fraud, voice clone fraud, and AI-enabled business email compromise — and the layered process, people, and technology defenses that stop it.
Read the guide → AI SecurityA practical LLM security checklist for engineers and security teams: prompt-injection defense, output handling, RAG isolation, least privilege, and the OWASP LLM Top 10.
Read the guide → AI SecurityIndirect prompt injection turns retrieved documents, web pages, and tool output into hidden instructions. Learn how RAG poisoning works and how to defend against it.
Read the guide → AI SecurityInsecure output handling explained for the enterprise: why unsanitized LLM output causes XSS, SQL injection, command execution, SSRF, and data exfiltration — and how to defend it.
Read the guide → AI SecurityAn LLM jailbreak bypasses a model's safety guardrails. Learn how jailbreaking LLMs works, why guardrails are probabilistic, and how to defend AI in depth.
Read the guide → AI SecurityWhat LLM guardrails are, the input and output controls that make them work, why they are probabilistic and not a security boundary, and how to design and test them.
Read the guide → AI SecurityA model extraction attack reconstructs your proprietary model through API queries. Learn extraction, model inversion, membership inference, and the defenses.
Read the guide → AI SecurityA clear enterprise guide to post-quantum cryptography: the quantum computing threat, harvest now decrypt later, NIST PQC standards, and a practical migration roadmap.
Read the guide → AI SecurityA practical guide to RAG security: the risks unique to retrieval-augmented generation — prompt injection, knowledge base poisoning, permission bleed, embedding leakage — and how to defend them.
Read the guide → AI SecurityShadow AI — employees using unsanctioned AI tools without oversight — is the AI-era evolution of shadow IT. A practical guide to the risks and how to govern it.
Read the guide → AI SecurityThe AI attack surface, mapped for the enterprise: models, prompts, RAG, agents, supply chain, and APIs — walked through the OWASP Top 10 for LLM Applications.
Read the guide → AI SecurityTraining data poisoning corrupts AI models at the source. Learn how data poisoning attacks and model backdoors work, the AI supply-chain risk, and how to defend.
Read the guide → AI SecurityWhat is AI security? A complete enterprise guide to securing AI systems: the AI attack surface, threats like prompt injection and data leakage, governance, and defense.
Read the guide → AI SecurityPrompt injection is the #1 LLM security risk. Learn how prompt injection attacks work, direct vs. indirect injection, real-world impact, and how to defend AI.
Read the guide → AI SecurityAn AI gateway is the inline control point between your apps and models — inspecting prompts, blocking injection, redacting PII, enforcing policy, and routing.
Read the guide → AI SecurityA practical guide to zero trust AI: applying never-trust/always-verify, least privilege, and assume-breach to prompts, retrieval, model output, and agents.
Read the guide →Crypto-agility is the ability to change cryptographic algorithms without re-architecting systems. A practical guide to PQC migration: CBOM, hybrid, FIPS 203/204/205.
Read the guide → EncryptionHarvest now, decrypt later is the strategy of capturing encrypted data today to break it once quantum computers arrive. Learn the real risk and the fix.
Read the guide → EncryptionML-KEM explained: how NIST FIPS 203 (CRYSTALS-Kyber) uses Module-LWE lattices for quantum-safe key encapsulation, its parameter sets, and where it fits in TLS.
Read the guide → EncryptionA technical guide to Deflected's post-quantum encryption stack: ML-KEM-1024, ML-DSA-87, SLH-DSA, hybrid X25519, AES-256-GCM and the NIST standards behind them.
Read the guide →A practical enterprise guide to answering the AI security questionnaire — SIG, CAIQ, and VSA frameworks, the categories buyers probe, and building a reusable evidence library.
Read the guide → AI GovernanceHow to build an AI governance framework: guiding principles, roles and RACI, an AI inventory, policies, lifecycle controls, and mapping to NIST AI RMF, ISO 42001, and the EU AI Act.
Read the guide → AI GovernanceISO 42001 explained: what the ISO/IEC 42001 AI management system standard is, its structure and Annex A controls, how it relates to 27001 and the EU AI Act, and the certification path.
Read the guide → AI GovernanceSOC 2 for AI companies: what the attestation is, Type I vs Type II, the Trust Services Criteria, AI-specific controls, a readiness roadmap, and how it maps to AI rules.
Read the guide → AI GovernanceA practical AI compliance checklist covering data governance, model governance, security controls, oversight, and framework mapping to SOC 2, NIST AI RMF, EU AI Act, and ISO 42001.
Read the guide → AI GovernanceA plain-English guide to the EU AI Act: its risk-based tiers, high-risk AI obligations, GPAI rules, the phased timeline, penalties, and a practical readiness checklist.
Read the guide → AI GovernanceA practical guide to the NIST AI RMF: what the framework is, its trustworthy AI characteristics, the GOVERN, MAP, MEASURE, and MANAGE functions, and how to operationalize it.
Read the guide → AI GovernanceA clear, enterprise guide to AI TRiSM (AI Trust, Risk and Security Management): its four pillars, why it matters, and how it complements NIST AI RMF, ISO 42001 and the EU AI Act.
Read the guide →How AI phishing works — flawless language, deep personalization, polymorphic content, deepfake voice, and multi-channel scale — and how enterprises build a layered defense.
Read the guide → FraudHow business email compromise AI attacks work: generative-AI phishing, cloned writing style, deepfake voice approvals, and the layered finance defenses that stop them.
Read the guide → FraudHow voice cloning fraud works, the enterprise attacks it enables — CEO wire fraud, help-desk resets, vendor payment changes — and the layered defenses that stop it.
Read the guide →An A–Z AI security glossary defining 60+ essential terms — prompt injection, jailbreak, RAG, guardrails, model poisoning, post-quantum crypto, and more.
Read the guide → BusinessAI security for startups: why early-stage teams carry an enterprise-scale attack surface, how security unlocks enterprise sales and funding, and a staged roadmap.
Read the guide → HealthcareProtecting PHI in AI: how LLM and RAG systems put protected health information at risk, HIPAA obligations, de-identification limits, and the safeguards that help.
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