AI Governance
Your Company Is Already Using AI. Just Not Safely.
Dave DuPont, Shane Emmons, and Future Point of View's Scott Klososky on why carriers stall at their third AI agent, and what separates a governance policy from day-to-day oversight.
AI Insights & News
AI Governance
AI Incident Response: A Plan for When the Model Is the Incident
An AI incident response plan for production LLMs, chatbots, and agents: a six-class incident taxonomy, severity tiers, a phased runbook, and roles.
AI Governance
How to Detect Shadow AI: Six Signals Your Existing Tools Already Have
Six signals your identity, network, endpoint, finance, and code systems already record, how to detect shadow AI with them, and why detection has a ceiling.
AI Governance
AI Acceptable Use Policy: What to Include, Plus a Template You Can Adapt
What an AI acceptable use policy should contain, how to roll it out, and where the rules actually get enforced. Includes a full 15-section template to adapt.
AI Governance
AI Audit Trail: The Evidence Carriers Need for Examiners, Courts, and the Board
A field specification for the AI audit trail insurance carriers now need: the model inventory examiners request first, the decision record, retention, bias testing, rate-filing explainability, Lokken discovery, and board reporting.
AI Governance
Reinsurance AI: How Reinsurers and Rating Agencies Are Underwriting Your AI
Reinsurers and rating agencies are already forming a view of the AI in your rating and claims stack. The treaty clauses, rating methodology, case history, and renewal evidence every carrier should understand.
AI Governance
The Future of AI and Claims: Live Panel at the 2026 PLRB Claims Conference
Swept AI CEO Shane Emmons joins a live panel on The Future of Insurance Podcast to discuss agentic AI in claims, AI drift, litigation risk, and the talent pipeline challenge.
AI Governance
The NAIC AI Bulletin: What It Requires, How the AI Evaluation Tool Tests It, and What Carriers Need on File
A carrier's guide to the NAIC AI bulletin: the AIS Program it requires, where states have adopted it, what the 12-state AI Systems Evaluation Tool pilot asked, the federal preemption fight, and the evidence to keep on file.
AI Governance
AI Governance for Mutual Carriers: The 2026 Advantage Stock Carriers Can't Copy
Mutual carriers have a structural AI governance advantage: shorter decision chains, policyholder-owners on the board, and no quarterly earnings pressure. Here is how to turn it into a staged, surplus-conscious AI program.
Enterprise AI
AI ROI: How Carriers Measure It, Prove It, and Stop Overpaying for It
63% of carriers have operationalized AI, yet most cannot prove AI ROI. A practical framework for measuring AI ROI, managing AI spend per user and department, and building a board-ready business case.
Enterprise AI
AI Insurance Liability: New CGL Exclusions, Silent AI Coverage, and What Every Enterprise Should Know
Your existing insurance probably doesn't cover AI failures anymore. New CGL endorsements CG 40 47 and CG 40 48 are resolving years of silent AI coverage by excluding generative AI claims from standard policies.
AI Governance
AI Governance for Insurance Carriers: Where AI Runs Across the Value Chain and What Regulators Expect
Insurance AI governance across underwriting, claims, catastrophe modeling, distribution, and cyber: where AI runs today, how its failures reach the loss ratio, and the evidence state regulators now expect carriers to produce.
AI Governance
AI Vendor Risk Assessment: What to Ask, What to Test, and When to Build Instead
A carrier's guide to AI vendor risk assessment: the questionnaire, how to test vendor claims on your own policies and data, eight contract clauses that survive a market conduct exam, the NAIC vendor registry, and when building beats buying.
AI Governance
Shadow AI: What It Is, Why It Leaks Data, and How Carriers Rein It In
Shadow AI is the AI your people use without approval, usually because it beats what they were given. What it is, the risks, what AI data leakage really looks like, and why sanctioning the right tools works where banning fails.
AI Governance
The CRI FS AI RMF: What 108 Financial Institutions Agree AI Risk Management Actually Requires
The CRI Financial Services AI Risk Management Framework defines 230 control objectives across four NIST AI RMF functions with a staged adoption model. Built by 108 financial institutions, it is the first industry-consensus AI governance standard for financial services.
AI Safety
GenAI Risk in Financial Services: What the FS AI RMF Says About Hallucinations, Deepfakes, and Prompt Injection
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.
AI Governance
State AI Regulations in 2026: Colorado, Texas, California, Illinois, and What's Coming
A practical guide to state AI regulations and state AI laws in 2026: the Colorado AI Act and Colorado's insurance rules, Texas TRAIGA, California SB 53, New York's RAISE Act, Illinois SB 315, privacy law, and how to build one multi-state compliance program.
AI Governance
Understanding Model Drift: Types, Detection, and Response
Even the best models degrade when incoming data shifts from training data. Understanding the types of drift, how to detect them, and how to respond determines whether your models remain reliable over time.
AI Governance
Essential ML Model Performance Metrics
Machine learning models fail silently. Unlike traditional software that crashes visibly, underperforming models continue producing outputs without obvious errors. The right metrics reveal problems before they cause significant harm.
AI Governance
EU AI Act Compliance: A Practical Guide for Enterprise AI Teams
Everything you need to know about EU AI Act compliance: what counts as an AI system, risk classifications, high-risk requirements, timelines, and a practical checklist for enterprise AI teams.
AI Governance
AI Governance Maturity Model: Assess and Advance Your Organization's AI Governance
A practical AI governance maturity model: 5 levels, an 8-dimension assessment matrix, a RACI, and a mapping to NIST AI RMF, ISO 42001, and the EU AI Act.
AI Operations
AI Evaluation: How to Test, Validate, and Trust Your AI Systems
A comprehensive guide to AI evaluation — methods, metrics, frameworks, and tools for testing and validating AI systems before and after deployment.
AI Governance
Agentic AI Governance: The Complete Guide to Governing Autonomous AI Agents
The complete guide to agentic AI governance for carriers: the reliability data, guardrails, runtime supervision, security, and audit trails autonomous AI agents need in production.
AI Governance
Debugging ML Models with Explainable AI
Machine learning models can have invisible bugs that traditional testing misses. Explainable AI techniques reveal data leakage, data bias, and other problems that undermine model reliability.
AI Supervision
Building Trustworthy AI: Navigating the Challenges and Future of Agentic Software with Shane Emmons
In this episode of The Innovators & Investors Podcast, host Kristian Marquez sits down with Shane Emmons, founder and CEO of Swept, to explore the complexities and challenges surrounding AI trust and reliability
AI Future
Swept AI Building the Trust Layer for Artificial Intelligence
Alex Mysinek sits down with Shane Emmons, Founder and CEO of Swept AI, to talk about the missing piece in the AI revolution—trust. We are creating a system that supervises, evaluates, and protects AI models to ensure safety, accuracy, and alignment.
Press Releases
Swept AI Raises $1.4M to Supervise the Next Generation of Autonomous Systems
Swept AI, a startup focused on supervising, interrogating, and optimizing autonomous AI agents, has raised $1.4M in pre-seed funding led by M25, with participation from Wellington Management Company, BuffGold Ventures, SPARK Capital, Service Provider Capital, The Unicorn Group, and angel investors.
AI Future
Navigating the AI Hype: Practical Observability and Realistic Expectations
This article explores the practical aspects of AI implementation, emphasizing the importance of observability, testing, and realistic expectations. It offers valuable insights for developers seeking to leverage AI effectively in solving real-world problems.
MCP
MCP Is Not the USB of AI—It’s Just HTTP
There’s a growing tendency in AI marketing circles to refer to MCP—the Model Context Protocol—as the “USB of AI.” The idea, presumably, is that it offers some kind of plug-and-play universal interface between language models and tools. But this metaphor is worse than lazy—it’s actively misleading.Let’s dig into why this comparison doesn’t work, and why we should be framing MCP for what it really is: the HTTP of agentic AI.