Perspectives

Discover how cybercriminals are turning the Flutter development framework into an attack vector, fueling a new wave of stealthy mobile malware.

Many AI safety and governance approaches break down once AI systems reach production. See how Alice and NVIDIA address this gap with an AI safety flywheel combining adversarial testing, guardrails, and real-time oversight to keep AI systems governed at scale.

A plain-language explanation of prompt injection: what it is, the five types, how it differs from jailbreaking, and where it shows up in real AI systems.

What LLM guardrails are, how they secure prompts, RAG, outputs, and agent tool calls at runtime, and how to measure them like production controls rather than prompt tweaks.

What AI guardrails are, the types that cover inputs, outputs, data, tools, and agents, and how to enforce policy before, during, and after each AI interaction.

What prompt injection attacks are, how direct, indirect, and agentic variants reach LLM apps and agents, the business impact, and the runtime defenses that contain them.

The controls for managing generative AI risk across prompts, RAG, agents, and outputs, before launch, at runtime, and after deployment.

How security teams turn AI governance into working controls: inventory, risk assessment, red teaming, runtime guardrails, monitoring, and the evidence that proves each one works.

A category map of AI security tools, from discovery and red teaming to runtime guardrails and governance, and how to choose controls for GenAI apps, LLMs, and agents.