Detect Hallucinations Using LLM Metrics
Hallucinations are outputs generated by LLMs that lack factual accuracy. Monitoring them is fundamental to delivering correct, safe, and helpful applications.
AI Safety & Guardrails
Techniques for building safe AI systems with runtime supervision, guardrails, bias protection, and risk mitigation.
23 articles & guides
Latest in AI Safety & Guardrails
Why Current AI Guardrails Are Security Theater
Most guardrails today are probabilistic systems policing other probabilistic systems. That's not defense in depth—it's multiplied failure modes. Here's what actually works.
AI Safety in Generative AI: Priorities and Practices
Safety should be a top priority in all AI endeavors. The true threat lies not in chatbot vulnerabilities but in AI systems synthesizing hard-to-find disruptive information.
AI Innovation and Ethics: Aligning Language Models with Human Values
As LLMs become more capable, aligning them with human values grows more complex. The path forward requires coordinated research across oversight, robustness, interpretability, and governance.
Adversarial Attacks on Machine Learning Models
Machine learning models can be fooled by carefully crafted inputs that appear normal to humans. Understanding adversarial attacks is essential for building secure AI systems.
The Guardrails-Velocity Trap: Why Speed and Safety Aren't a Tradeoff
The conventional wisdom says you can move fast or move safely. That's a false choice. Here's how to build AI systems that are both fast and trustworthy.
Lawyers, AI Won't Take Your Job, But It Could Get You Fired
AI won’t replace lawyers, but careless use can jeopardize careers. Treat AI as an assistant to speed research, review, and drafting—while enforcing oversight to catch drift, verify outputs, and protect client data. Maintain monitoring, training, and strict privacy controls. Tools like Swept.AI help detect drift early so you stay compliant and in control.
Why Every AI Race Ends In Expensive Disasters
Organizations rushing AI to market without proper validation face millions in avoidable losses. This analysis examines real cases like IBM's $4 billion Watson Health writedown and reveals why 42% of AI projects now fail before production. Learn the difference between structured and unstructured AI deployment, discover proven validation frameworks that prevent costly failures, and understand how thorough testing actually accelerates successful implementation rather than delaying it.
Guides & Definitions
What are AI Guardrails?
AI guardrails are safety mechanisms that constrain AI system behavior, preventing harmful outputs, enforcing policies, and ensuring AI operates within acceptable boundaries.
What are AI Hallucinations?
AI hallucinations occur when models generate confident but factually incorrect, fabricated, or nonsensical outputs—a fundamental challenge for enterprise AI deployment.
What is AI Adversarial Testing?
Adversarial testing simulates malicious or tricky inputs to measure how your AI behaves so you can fix weaknesses before customers or attackers find them.
What is AI Bias and Fairness?
AI bias occurs when models produce systematically unfair outcomes for certain groups. Fairness is the practice of detecting, measuring, and mitigating these disparities.
What is AI Red Teaming?
AI red teaming is structured, adversarial testing of AI systems using attacker-like techniques to surface failure modes, vulnerabilities, and unsafe behaviors so you can fix them before real-world damage occurs.
What is AI Safety?
AI safety ensures AI systems behave predictably, align with human intent, and resist causing harm. Learn how Swept makes AI safety practical for enterprises.
What is Prompt Injection?
Prompt injection is when an attacker embeds malicious instructions in plain language so your LLM or agent follows their orders instead of yours.
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