AI guardrails are not something you want; they’re something you need. The process for implementing AI guardrails isn’t even tricky, so there’s no excuse not to have them. And if you know anything about AI, you’ll know that it isn’t exactly a technology that always has a smooth integration into mainstream use. From bias to outright security risks, there are many potential risks to guard against.
Read on to learn about AI guardrails in more detail and the process for implementing them.
What Are AI Guardrails?
AI guardrails could be your lifesaver.
They’re predefined rules and protocols designed to ensure that AI systems operate within safe and acceptable boundaries. And if you have a look at how some AI systems have performed and the obscure results they’re producing, you’ll know that the correct AI guardrails were blatantly not in place. And these were big companies like Google and OpenAI – so it’s not as easy as you would think.
Guardrails act as a protective mechanism, preventing AI from making decisions or taking actions that could lead to undesirable outcomes such as incorrect data. Again, look at some of the recent AI search issues from Google to see the true meaning of a less-than-desirable outcome.
AI guardrails are essential. They’ll maintain:
- AI system integrity
- Fairness
- Security
- Functions
They’ll also help mitigate risks like data biases, security vulnerabilities, and ethical concerns. However, If you don’t know the process for implementing the correct AI guardrails, it’s pointless to put in the effort in the first place. You ought to do it, but to do it the right way.
Understanding Your Guardrails
Understanding your AI guardrails should involve a thorough assessment of the AI system’s purpose, potential impacts, and areas where it could go wrong…which is all of them.
You should be analyzing everything from the securitylegal, ethical, technical, and business processes to understand where guardrails are necessary. And more importantly, like Google clearly didn’t do, you should analyze how individual use can influence outputs. One of the reasons Google gave for their recent AI mess is because they said they couldn’t prepare for how millions of people using their AI system would influence results. But the thing is, Google, you can prepare for it…with your guardrails.
And it’s not just about security and mitigating the risk of the less-than-desirable outcomes. You can tailor guardrails to protect and enhance the AI system’s performance.
Input Guardrails
Input guardrails should be your first focus. If you look at Google’s recent issues with its AI Overviews, the entire issue stemmed from the system pulling search result answers directly from Reddit forums – pretty poor programming, if you ask us.
Data fed into the system should be accurate, unbiased, and appropriate for the intended use. That involves setting criteria for data quality, such as verifying the source, checking for biases, and ensuring relevance.
Implementing strict data validation processes can also help filter out problematic data before it reaches the AI model. Think about techniques like data anonymization and encryption to further protect sensitive information from being misused. With these robust input guardrails, organizations can significantly reduce the risk of feeding their AI systems with flawed data.
Output Guardrails
Then there’s the output. If anything, we’d say output guardrails are more essential than input guardrails.
Output guardrails should monitor and control the results generated by AI systems. Again, in the case of Google, with Gemini and AI Overview, you would think there were no input or output guardrails at all. These guardrails ensure the AI outputs are accurate, fair, and within the expected boundaries.
Creating AI mechanisms to regularly review and validate AI outputs is essential. Think automated checks for consistency, compliance with ethical standards, and alignment with business objectives.
And please, whatever you do, set up alert triggers for obscure results for fast resolutions.
Guardrail Thresholds
Guardrail thresholds are the acceptable limits for AI system operations.
These thresholds ensure AI systems don’t exceed their intended scope or generate outputs leading to potential security risks. Establishing guardrail thresholds involves setting quantitative and qualitative benchmarks based on historical data, expert insights, and regulatory guidelines. And it’s not enough to set a threshold and never look at it again. They must be regularly updated to meet new thresholds as information evolves.
So, can you now see how essential AI guardrails are? Ultimately, the correct AI guardrails should steer AI toward compliant behavior and desirable outcomes. But, as you can see with some of the recent AI dramas, not all companies know how to implement them properly.