Category: Cybersecurity & Governance - Page 3
Red Teaming Prompts for Generative AI: Finding Safety and Security Gaps
Learn how to identify and fix safety gaps in generative AI using red teaming strategies. Covers prompt injection, automation tools, and regulatory compliance.
Risk and Controls for Generative AI: Policies, Approvals, and Monitoring Strategy
A comprehensive guide to managing risk and controls for generative AI in 2026. Covers NIST frameworks, ISO certifications, policy enforcement, and continuous monitoring strategies.
Governance Policies for LLM Use: Data, Safety, and Compliance
Governance policies for LLM use now require strict controls on data, safety, and compliance across federal and state systems. Learn how agencies are implementing them-and where they’re falling short.
Incident Response Playbooks for LLM Security Breaches: What Works and What Doesn’t
LLM security breaches require specialized response plans. Learn how incident response playbooks for prompt injection, data leakage, and safety breaches work - and why traditional cybersecurity tools fail to stop them.
Funding Models for Vibe Coding Programs: Chargebacks and Budgets
Vibe coding slashes development time but creates unpredictable costs. Learn how chargebacks happen, why flat-rate plans fail, and how to build real budgets for AI-driven development.
Communicating Governance Without Killing Velocity: Dos and Don'ts in Software Development
Learn how to communicate governance in software teams without slowing down velocity. Discover practical dos and don'ts from top tech companies that balance compliance with developer autonomy.
Liability Considerations for Generative AI: Vendor, User, and Platform Responsibilities
In 2026, generative AI liability is no longer theoretical. Vendors, users, and platforms all share legal responsibility when AI causes harm. New laws in California and New York are enforcing transparency, disclosure, and accountability across the AI supply chain.
Why Functional Vibe-Coded Apps Can Still Hide Critical Security Flaws
Vibe-coded apps built with AI assistants may work perfectly but often hide critical security flaws like hardcoded secrets, client-side auth bypasses, and exposed internal tools. These flaws evade standard testing and are growing rapidly - here’s how to spot and fix them.
When to Use Open-Source Large Language Models for Data Privacy
Open-source large language models give organizations full control over sensitive data by running AI on their own servers. They’re the best choice for finance, healthcare, and government teams that can’t risk leaking data to third parties.
Databricks AI Red Team Findings: How AI-Generated Game and Parser Code Can Be Exploited
Databricks AI red team uncovered critical vulnerabilities in AI-generated game and parser code, showing how prompt injection, data leakage, and hallucinations can be exploited. These aren't theoretical risks-they're happening in real systems today.
Security Operations with LLMs: Log Triage and Incident Narrative Generation
LLMs are transforming SOC operations by automating log triage and generating clear incident narratives, reducing alert fatigue and response times. Learn how they work, their real-world accuracy, risks, and why humans still must stay in the loop.
Preventing RCE in AI-Generated Code: How to Stop Deserialization and Input Validation Attacks
AI-generated code often contains dangerous deserialization flaws that lead to remote code execution. Learn how to prevent RCE by replacing unsafe formats like pickle with JSON, validating inputs, and securing your AI prompts.
