AI Ethics & Safety

Prompt Injection Attacks: What They Are and How to Defend Against Them

As AI systems move from simple chatbots to tool-using agents and automated workflows, a new class of security risk has emerged: prompt injection attacks. Unlike traditional exploits that target code, prompt injection targets instructions themselves—turning language into an attack surface. If you build with LLMs, use AI agents, or connect models to tools, understanding prompt […]

Prompt Injection Attacks: What They Are and How to Defend Against Them Read More »

Sanitizing Inputs for AI APIs: A Practical Guide for Developers

AI APIs are incredible for summarizing documents, generating content, and automating workflows—but they’re also a fast way to leak sensitive information if you don’t sanitize inputs properly. In practice, “data sanitization” means removing (or transforming) anything that could identify a person, expose credentials, reveal proprietary content, or unintentionally grant access—before the request ever reaches the

Sanitizing Inputs for AI APIs: A Practical Guide for Developers Read More »

Adversarial Attacks on ML Models: Techniques and Defences

Machine learning models are everywhere—from recommendation engines to autonomous systems. However, as models become more powerful, they also become more vulnerable. One of the most critical yet under-discussed threats today is adversarial attacks on ML models. In this article, we’ll explore what adversarial attacks are, why they matter, the most common techniques used by attackers,

Adversarial Attacks on ML Models: Techniques and Defences Read More »

Privacy-First AI Tools: The Best Alternatives That Keep Your Data Local

For years, convenience has won the battle against privacy. We upload documents, prompts, and personal ideas into cloud-based AI tools—and hope for the best. However, that mindset is changing. As AI adoption accelerates, privacy-first AI tools are emerging as powerful alternatives that keep your data local, offline, or fully under your control. Instead of shipping

Privacy-First AI Tools: The Best Alternatives That Keep Your Data Local Read More »

How to Build Content Moderation Into Your AI Application

As AI-powered applications become more capable, they also become more responsible. From chatbots and comment systems to AI agents and automation workflows, content moderation is no longer optional—it’s foundational. If your AI app accepts user input or generates text, images, or code, you must think about safety, abuse prevention, and trust from day one. The

How to Build Content Moderation Into Your AI Application Read More »

Auditing Your AI Outputs: Building a Quality Control Process

As AI becomes central to everyday workflows, creators, professionals, and teams are discovering a hard truth: AI doesn’t guarantee accuracy — you do.Whether you’re generating content, coding, summarizing reports, or building automations, you need a repeatable audit process to review and validate AI outputs before they go live. This article breaks down how to build

Auditing Your AI Outputs: Building a Quality Control Process Read More »

Training AI to Be Safe: Inside RLHF and Constitutional AI

Modern AI models seem incredibly capable — they answer questions, write essays, generate code, and act as creative partners. But beneath that smooth interaction lies a much harder challenge: teaching AI systems how to behave safely. Two of the most important alignment strategies used today are RLHF (Reinforcement Learning from Human Feedback) and Constitutional AI.

Training AI to Be Safe: Inside RLHF and Constitutional AI Read More »