AI Product Safety & Responsible AI: The 6 Guardrails Every PM Needs

Normal software does what its code says. An AI product does what it predicts, and that can be wrong, unsafe or surprising. In this video, Vineet Kumar Sehrawat explains why that difference calls for stronger guardrails and what product managers should put in place. His definition is simple: an AI product is safe when it resists misuse and survives mistakes.

Why AI needs more than ordinary testing

Traditional software fails in ways you can list and test. A model can fail in ways nobody predicted, on inputs nobody imagined, and attackers can steer it with plain language. Because a model is not predictable, safety has to be designed around it, tested, and watched after launch rather than only before. The video asks for more testing, more layers of protection, and ongoing monitoring.

Guardrails against wrong and harmful output

Two of the six guardrails deal with what the model says.

• Hallucination controls reduce confident wrong answers. They ground answers in trusted sources, ask for citations, and let the product say it does not know.
• Content safety filters harmful input and output. It needs testing against people who actively try to bypass it.

The video notes that neither control is perfect, so they should be layered.

Guardrails on access and data

Permissions decide what the AI can see and do. The AI should get the least access it needs and respect each user's own rights, so it never shows one person another person's data. Privacy means collecting less, protecting what you keep, and being clear about what is sent to model providers.

Guardrails that keep people in control

Human approval puts a person in front of risky actions such as sending money, deleting data, or sending messages. Transparency tells users they are using AI, what it can and cannot do, and when it may be wrong. Together these keep trust because people can see, check and override what the system does. Each of the six guardrails limits a different way the product can go wrong.

Making responsible AI a habit

The video calls responsible AI a habit, not a banner. It lists the practices that make it real:

• Assess risk before you build.
• Red-team the product with people who try to break it.
• Write down what the product should never do.
• Give users a way to report problems.
• Assign an owner who can pause the feature when something goes wrong.

Key takeaways

• Safe means the product resists misuse and survives mistakes.
• Models are unpredictable, so safety must be designed in, tested, and monitored after launch.
• The six guardrails are hallucination controls, content safety, permissions, privacy, human approval, and transparency.
• No single control is perfect, so layer them.
• Give the AI the least access it needs and never let one user see another's data.
• Require a person to approve high-risk actions like sending money or deleting data.
• Assign a feature owner with the authority to pause the feature.
• A rare but serious harm usually deserves a guardrail before a common but minor one.

Try it yourself

Describe your AI feature to Claude and ask it to list the top 10 ways it could cause harm or embarrassment, rating each by likelihood and severity and proposing one guardrail for each. Ask which action should always need human approval and what you should tell users about the feature's limits. Start with the highest-severity items first. The video is part of the Project Medhavy series.