AI is starting to touch evidence, money, customer data, business tools, and production code.
Before shipping AI, answer one question:
Can this AI be tricked, trusted, tested, and controlled?
Source: Stanford HAI AI Index 2026
If you ship AI in production, you should be able to explain each of these in one sentence to a non-technical stakeholder.
A public list of common risks for AI agents that can use tools, remember context, and act on their own.
Whether an AI agent can be tricked, misuse tools, overstep permissions, remember bad information, or take actions the owner did not approve.
If an AI agent can act inside a real product or company system, it needs limits before users rely on it.
A public list of common risks for apps built with large language models.
Whether a chatbot can be tricked, leak private data, expose hidden instructions, trust unsafe content, or use too much freedom.
Most AI apps start as chatboxes. They still need security rules before they touch real users or private data.
A public map of attack patterns used against AI systems.
How attackers try to trick, poison, steal from, or manipulate AI systems.
Builders need to know what real attacks look like before they decide what to test.
A voluntary guide many organizations use to manage AI risk.
Whether a team has a clear way to name, measure, review, and reduce AI risk.
It gives teams a shared process for deciding when AI is safe enough to use.
Three recent examples of why AI systems need limits before they touch tools, data, or code.
Microsoft disclosed vulnerabilities where prompt injection could cause an AI agent framework to run code on the machine hosting it.
If AI can use tools or run scripts, bad instructions can become real actions.
Do not let AI tools run commands freely. Limit what they can do, log what they do, and require approval for risky actions.
A rogue internal AI agent at Meta reportedly exposed sensitive company and user data to employees who did not have permission to access it.
Even internal AI tools can break approval boundaries if permissions are not tightly controlled.
AI agents that can share, publish, or access company data need strict limits and human approval.
Reports said an experimental AI agent tied to Alibaba-affiliated research used training resources to mine cryptocurrency during testing.
AI agents can behave in unexpected ways when their goals, tools, and limits are not controlled tightly enough.
Agents need containment, monitoring, and hard limits before they are given resources or system access.
None of this is exotic. These are the operating rules I use.
OWASP Top 10 for Agentic Applications and NIST AI RMF are free, public, and the floor before any agent touches customer data, payments, tools, or production code.
No AI agent should reach real users without testing. If it can touch money, data, tools, or customer accounts, it needs stricter review.
If AI is part of a hiring, legal, medical, or financial decision, the person affected should be told and able to ask for human review.
If a company deploys AI to customers, it should assume responsibility for what the AI says and does. Do not blame the model when users are harmed.
Take over repetitive tasks, but leave judgment, review, and final decisions to people.
Independent researchers often find serious AI security issues. They need safe ways to report them.
Every load-bearing claim above traces to one of these public sources.
I build AI software that sets clear limits before AI touches customers, data, or production code.
Have a case study, a policy proposal, or a correction for this page? Same address: lukatasolutions@gmail.com.