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> This case is an example of the new risks organizations face when starting to leverage the power of AI more broadly, as more of their engineers now work with m

> This case is an example of the new risks organizations face when starting to leverage the power of AI more broadly, as more of their engineers now work with massive amounts of training data. It seems like a stretch to associate this risk with AI specifically. The era of "big data" started several years before the current AI boom.

numbsafari 3y ago
This is the risk of using, checks notes, Azure and working with Microsoft. Except there is no risk for them. They've proven time and again they have major security snafus and not be held accountable.
- robertlagrant 3y ago
  They have all the regulatory paperwork in place, so it must be fine.
  - datavirtue 3y ago
    They are also the top line investment for the majority of mutual and pension funds. Don't crab too much, they are funding your retirement.
- eddythompson80 3y ago
  Virtual networks are a nightmare to setup and manage in Azure which is why everyone just takes the easy path and not bother. Almost every Azure service we deal with has virtual networks as an after thought because they want to get to market as quickly as possible, and even to them managing vnets is a nightmare. Not to excuse developers/users though. There are plenty of unsecured S3 buckets, docker containers, and Github repos that expose too much "because it's easier". I've had a developer checkin their ftp creds into a repo the whole company has access to. He even broke the keys up and concat them in shell to work around the static checks "because it's easier" for their dev/test flow.
mavhc 3y ago
With big data comes big responsibility
intrasight 3y ago
Agreed. It should say "new risks organizations face when starting to leverage the power of Azure" or "the power of cloud computing". But as clickbait worthy a title.
Phileosopher 3y ago
AI has magnified the use cases, though. Before, Big Data was an advertising machine meant to tokenize and market to every living being on the planet. Now, machine learning can create "averaged" behavior of just about anything, given enough data and specificity.
omgJustTest 3y ago
This comment is a good bit of rationalization, and whichever the categorical mismatch you feel is happening, it misses the overarching point, the focus should be on the broader systemic issues: data security is not a first or second tier priority to "big data" or "AI"... largely because there's no cost to doing it poorly.
deleted 3y ago
[deleted]
acdha 3y ago
The second clause covers that: this isn’t an AI problem, just as it wasn’t a big data problem when the same kinda of things happened a decade ago. It’s a problem caused when you set up something new outside of what the organization is used to and have people without appropriate training asked to make security decisions: I’d bet that this work was being done by people who were used to the academic style, blending personal and corporate use on the same device, etc. and simply weren’t thinking of this class of problem. The description sounds a lot like the grad students & postdocs I used to support – you’d see some dude with Steam on his workstation because it faster than his laptop and since he was in the lab 70 hours a week anyway, why not 90? The challenge for organizations is figuring out how to support research projects and other experiments without opening themselves up to this kind of problem or stymieing R&D.

numbsafari 3y ago

This is the risk of using, checks notes, Azure and working with Microsoft. Except there is no risk for them. They've proven time and again they have major security snafus and not be held accountable.

robertlagrant 3y ago
They have all the regulatory paperwork in place, so it must be fine.
- datavirtue 3y ago
  They are also the top line investment for the majority of mutual and pension funds. Don't crab too much, they are funding your retirement.
eddythompson80 3y ago
Virtual networks are a nightmare to setup and manage in Azure which is why everyone just takes the easy path and not bother. Almost every Azure service we deal with has virtual networks as an after thought because they want to get to market as quickly as possible, and even to them managing vnets is a nightmare. Not to excuse developers/users though. There are plenty of unsecured S3 buckets, docker containers, and Github repos that expose too much "because it's easier". I've had a developer checkin their ftp creds into a repo the whole company has access to. He even broke the keys up and concat them in shell to work around the static checks "because it's easier" for their dev/test flow.

mavhc 3y ago

With big data comes big responsibility

intrasight 3y ago

Agreed. It should say "new risks organizations face when starting to leverage the power of Azure" or "the power of cloud computing". But as clickbait worthy a title.

Phileosopher 3y ago

AI has magnified the use cases, though. Before, Big Data was an advertising machine meant to tokenize and market to every living being on the planet. Now, machine learning can create "averaged" behavior of just about anything, given enough data and specificity.

omgJustTest 3y ago

This comment is a good bit of rationalization, and whichever the categorical mismatch you feel is happening, it misses the overarching point, the focus should be on the broader systemic issues: data security is not a first or second tier priority to "big data" or "AI"... largely because there's no cost to doing it poorly.

deleted 3y ago

[deleted]

acdha 3y ago

The second clause covers that: this isn’t an AI problem, just as it wasn’t a big data problem when the same kinda of things happened a decade ago. It’s a problem caused when you set up something new outside of what the organization is used to and have people without appropriate training asked to make security decisions: I’d bet that this work was being done by people who were used to the academic style, blending personal and corporate use on the same device, etc. and simply weren’t thinking of this class of problem. The description sounds a lot like the grad students & postdocs I used to support – you’d see some dude with Steam on his workstation because it faster than his laptop and since he was in the lab 70 hours a week anyway, why not 90? The challenge for organizations is figuring out how to support research projects and other experiments without opening themselves up to this kind of problem or stymieing R&D.