Techniques for building safe AI systems with runtime supervision, guardrails, bias protection, and risk mitigation.
The FS AI RMF is the first sector-specific framework to codify GenAI risks within financial regulatory context. Learn how it addresses hallucinations, prompt injection, deepfakes, and agentic AI with specific control objectives.
Prompt injection is the number one threat to LLM applications according to OWASP. Testing for vulnerability before deployment is essential.
Hallucinations are outputs generated by LLMs that lack factual accuracy. Monitoring them is fundamental to delivering correct, safe, and helpful applications.
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.
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.
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.
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 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.
AI guardrails are safety mechanisms that constrain AI system behavior, preventing harmful outputs, enforcing policies, and ensuring AI operates within acceptable boundaries.
AI hallucinations occur when models generate confident but factually incorrect, fabricated, or nonsensical outputs—a fundamental challenge for enterprise AI deployment.
Adversarial testing simulates malicious or tricky inputs to measure how your AI behaves so you can fix weaknesses before customers or attackers find them.
AI bias occurs when models produce systematically unfair outcomes for certain groups. Fairness is the practice of detecting, measuring, and mitigating these disparities.
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.
AI safety ensures AI systems behave predictably, align with human intent, and resist causing harm. Learn how Swept makes AI safety practical for enterprises.
Prompt injection is when an attacker embeds malicious instructions in plain language so your LLM or agent follows their orders instead of yours.
Real-time supervision and guardrails to keep AI on spec in production.
Policies, frameworks, and supervision strategies for governing AI systems at enterprise scale.
32 articlesSupervise AI performance in production with observability, drift detection, and operational monitoring.
7 articlesMethods and tools for rigorously evaluating AI models before deployment and supervising them after.
57 articlesNavigate AI regulations, compliance requirements, and audit readiness with continuous supervision.
9 articlesSupervising autonomous AI agents with trust frameworks, safety boundaries, and multi-agent oversight.
14 articlesNavigate ISO 42001, NIST AI RMF, and emerging AI regulations with practical compliance guidance.
19 articlesEvaluating, supervising, and governing AI agents in customer service — from deployment readiness to continuous trust.