AI has created a level of mistrust and dislike for any technology that would have been unimaginable 5 years ago. Job losses and total insensitivity to all other community issues created by AI have made it much worse.
AI security, in particular, is now a real issue and making a lot of headlines. Anthropic 5’s suddenly famous mythos fable deals with current and emerging risks of AI.
The other obvious problem is less obvious. The AI sector, in its total lack of wisdom, is doing absolutely nothing to secure civilization as a whole for AI. Problems are visible everywhere, and fixes are invisible.
This is where confidential computing comes in modestly, not on a white stallion, but on NVIDIA’s quirky press release about NVIDIA’s partnership with Apple’s Private Cloud Compute.
It’s like finding the cure for all the crap in the weather report.
You really have to wonder who is looking out the window when AI storms blow.
Confidential Fundamentals of Computing
As a rule, you never hear about what’s going on any subject in the news. Whatever is going well with AI, if anything, let alone AI safety, seems to be a non-issue.
Confidential computing, in fact, has been receding into the background. It hasn’t exactly been thundering from the headlines.
The big threat perceived by AI is a super-bot capable of bypassing conventional security. This relates to everything from basic privacy to financial management. This is pretty much the wrong planet for the current threats. It is essential data that is at risk.
Confidential computing it just happens to be based on data protection while it’s running. When it is more sensitive and revealing in the form of transactions, for example, or when using key security data entry.
If you’re seeing an obvious fix for managing the most basic elements of security and data when it’s most vulnerable, bingo. thankfully, NVIDIA has some specific information about this process.
Confidential computing comes with some terminological baggage, but it’s pretty simple. “Trusted runtimes” are the security mode and processes that the confidential computer addresses. Unauthorized parties cannot access or modify the code in use or after execution.
These safeguards are called “isolation” and can be applied to virtual machines, applications, and functions. It would be almost impossible to avoid these security measures in the process.
These security features can be operated on CPU and GPU. There doesn’t seem to be any real limit to the range of confidential IT measures you can put on a system.
AI Security 101 and the downside of all the brainless
The idea of banning executable intrusions from permissions is not new. It’s been at least a decade. Microsoft introduced it as an OS feature around Windows 7.
Prohibition of strikes during the active process IS new. It is a real threat to malware functions. It is very practical. AI has raised the security bar to unheard of values.
Confidential computing can be the broad spectrum solution for almost everything that is wrong with global cyber security on so many levels. It can be used for everything from national security to basic purchases. The current state of cyber security and cyber espionage damage is at an all-time high. Compromised security is now a global industry and it’s all driven by AI.
Confidential computing may be the best possible selling point for security management.
The PR image disaster that AI is turning into can be summed up in the same two words, “confidential computing.”
People don’t want to worry about their super valuable IP being hijacked character by character while it’s in the process. They don’t want their potential years of work on brand new things to be cloned 5 seconds after they succeed.
How hard can it be to understand this? This image problem is critical to the adoption of future AI in all its forms. A real fix should be very visible. People talk about “consumer trust” as if it were a given. It’s not, and with the current chaotic state of AI introduction, it’s absolutely essential.
When does Confidential Information go into circulation?
Bringing confidential computing into the mainstream is the next crucial step. It’s happening at the enterprise and Cloud levels. Apple, NVIDIA and Google have strengthened their security technologies in a working frame.
This has probably been going on for some time, and higher levels of interaction in AI security have not been evident. You don’t have to explain your security measures, but they should be like a good watchdog, where you can see them.
The perceived risks of AI and its many actual disasters have made it critical to ensure that AI security is as common and understandable as SSL for ordinary use.
At the consumer level, confidential computing should be an everyday thing that you can use and feel confident using. It should assure businesses that the security of their highly vulnerable systems are actually under real-time management. It looks like the confidential account is ready to use.
