The Good News About AI People are using generative AI to see information in a more conversational way.
Generative AI tools can listen and respond to voice input, a popular alternative to typing text into a search engine.
In some forward-thinking organizations, it's even being applied to automate and innovate everyday tasks, like internal help desks.
It's important to remember that many of the most important and exciting use cases are not actually coming from generative AI. Advanced AI/ML models are helping solve some of the biggest problems facing humanity - things like developing new drugs and vaccines.
Enabling customers in the healthcare, medical, and life sciences fields to securely implement AI means helping them solve those big problems.
We have nearly 100 data scientists working on AI/ML algorithms every day, and we have released more than 50 models in support of stopping threats and preventing exfiltration of sensitive data from insiders or attackers who have infected insiders.
Security problems that were intractable are now solvable using AI/ML. For example, attackers have been stealing sensitive data in innovative ways, lifting secrets from virtual whiteboards or concealing data in images by emailing images embedded with sensitive information to evade common security tools.
An attacker could access an exposed repository with credit card images that are hazy or have a glare that traditional security may not recognize, but advanced ML capabilities could help catch.
These kinds of sophisticated attacks, enabled using AI/ML, also cannot be stopped without the use of AI/ML. The Bad News About AI Every technology can be used for good or for bad. Cloud today is both the biggest enabler of productivity and the most frequently employed delivery mechanism for malware.
Hackers are already using generative AI to enhance their attack capabilities - developing phishing emails or writing and automating malware campaigns.
Attackers don't have much to lose nor to worry about how precise or accurate the results are.
If attackers have AI/ML in their arsenal and you don't, good luck.
You need tools, processes, and architectures to protect yourself.
Balancing the good and bad of AI/ML means being able to control what data you're feeding into AI systems and solving the privacy issues to securely enable generative AI. We are at an important crossroads.
While its intention is to give guidance to federal agencies on AI systems testing and usage, the order will have ample applicability to private industry.
As an industry, we must not be afraid to implement AI and must do everything possible to thwart bad actors from applying AI to harm industry or national security.
Some applications will require high precision and accuracy as well as access to sensitive data, but others will not.
Generative AI hallucinations in a medical research context would deter its usage.
Error rates in more benign applications may be OK. Classifying how you're using AI can help you target the low-hanging fruit - the applications that aren't as sensitive to the tools' limitations.
To responsibly achieve any of those aspirational outcomes from generative AI or broader AI/ML models, organizations must first ensure they can protect their people and data from the risks inherent to these powerful tools.
This Cyber News was published on www.darkreading.com. Publication date: Mon, 11 Dec 2023 15:00:06 +0000