What happens in our organization if an AI system makes a decision that harms a customer or partner?

Define accountability before an incident. Who is responsible — the deployer, the configurator, or the vendor? Clear accountability prevents blame-shifting and ensures corrective action.

Do we have a process for detecting when our AI systems are behaving in ways we did not intend?

Monitoring for output drift, unexpected patterns, and edge case failures catches misalignment early. If you are not monitoring, you are assuming correctness without evidence.

How do we balance the pressure to deploy AI quickly with the need to deploy it safely?

Deploy quickly for low-risk use cases where errors are cheap and reversible. Deploy carefully for high-stakes applications. The mistake is applying the same pace to both.

Safety, Risk & Governance

AI safety and alignment

By Mark Ziler · Last updated 2026-04-05

AI safety in a business context means ensuring AI systems do what you intend and nothing you do not intend. This is not about sci-fi scenarios — it is about practical guardrails. Can the agent access data it should not? Will it send an email to the wrong person? Could it make a financial decision without approval? Safety is built into well-designed agents through defined scopes (what data it can access), approval gates (what actions require human sign-off), and audit trails (what it did and why). Safety is not an add-on. It is architecture.

Go deeper

Your AI agent just emailed a customer a service quote with a 40% discount that nobody authorized. The agent had access to the email system and the pricing database but no rule saying 'discounts above 15% require manager approval.' The AI didn't malfunction — it optimized for the goal you gave it (close the deal) without the constraints you forgot to set. That's an alignment problem, and it cost you $6,000 in margin on a single transaction.

The trap most companies fall into is testing AI on what it should do and forgetting to test what it shouldn't do. Your QA tested 'can the agent send a quote?' but nobody tested 'what happens if the agent decides a big discount will improve customer satisfaction?' Every action your AI can take needs a boundary: what data can it read, what it can modify, what requires a human in the loop, and what it's explicitly forbidden from doing.

Questions to ask

For every AI agent we deploy, have we defined not just its capabilities but its constraints — what it explicitly cannot do?
Do we have approval gates on high-impact actions (financial transactions, customer communications, data modifications)?
When was the last time we tested our AI agents with adversarial scenarios — inputs designed to push them past their intended boundaries?