Okay, so here’s the point. Artificial Intelligence has had its extremely long day in the sun, and the repeated rhetoric has become tedious to us all by now. If you hear another person uttering the term “Chat GPT” at a social gathering, you are probably going to be en route to spilling your wine on their tuxedo or gown.
But: here’s the thing. All this time that you’ve been rolling up your eyes in sheer repetition induced boredom of the AI hype machine, there has been a silent phenomenon lurking behind the dark corners of the technology world, especially that of enterprise computing. The somewhat mysterious phenomenon includes a mix of mind-bending technological advances and the kind of security that is resminiscent of the security paranioa usually reserved for the civilian bunkers of the cold war era.
This, dear reader, is your introduction to the world of secure Gen AI implementations (raising eyebworws already, aren’t we all?) In this world, data scientists are performing an elaborate orchestration between innovation and data privacy/risk mitigation (now that we all know that). Not unlike trusting a gossipy teenager to keep classified information a secret at their next party. But there’s a whole lot more at play here.
What’s At Stake, Really?
To begin with, allow us to present some statistics that will shake up any CISO from his power nap. According to the latest data breach report from IBM, the average cost-to-company of a data breach reached $4.45 million in 2023- an all time high. That’s pretty much the entire amount of a successful startup’s capital requirements.
However, the most interesting part about the whole thing is the phenomenon that the data industry is currently experiencing – not very different from a tightrope walk, on this time, the rope being the AI industry, which is all, rather almost dependent on data. Another statistic from Gartner, for example, shows that 79% of enterprises are either exploring or implementing Gen-AI solutions, however 68% among the very same ones have expressed severe concerns about data privacy and security. The analogy of going skydiving while suffering from vertigo is perhaps the most apt method to describe the situation.
The Assistance Conundrum
Open AI’s ChatGPT being the biggest example here – try remembering the first time you used it and found it to be the magic wand you had been searching for all your career- only to later discover that the LLM was pre-trained on pretty much all the data including your data, that it could get its hands on? Yes, at the time, it did indeed send shockwaves across the enterprise computing world at the speed of lightning, exposing the fundamental question – How does one leverage the power of Gen AI without inadvertently turning their proprietary data into into training fodder for the next ChatGPT model release?
A promminent Data Scientist, Dr. Sara Chen, explains it, “Think of LLMs as parrots with a perfect memory. You definitely want to avail their capabilities, while simultaneously ensuring that they’re not revealing your trade secrets to the next user who asks”.
Architecting Trust in GenAI – Keeping Your Enterprise Data Safe
For our readers interested in the technical aspects of how the digital fortress that we often address can be used to safeguard their enterprise data from the generic GenAI implementations today, here are our pro tips that will keep you well ahead not only from your competitors, but keep you ahead in leaps and bounds
Enterprise Specific Local Model Deployment- Rather than externalizing confidential enterprise level data through APIs just for the ease and convenience of using ready-to-plug-in LLMs we recommend using SLMs , which are specialized in your industry, and preferably developed in-house or getting them custom developed for your business. According to a recent McKinsey report, businesses that use Local Model Deployment report an overall 43% better data security outcomes while, simultaneously maintaining approximately 85%+ of the functionalities of standard LLM models (to be precise, LLM’s have only a 13% advantage over enterprise SLMs as of today, and this gap is only expected to get shorter over time)
Data Sanitization – Following the age-old garbage in-garbage out principles, ensuring that only properly sanitized data. Preprocessed through sophisticated systems, and thoroughly scrubbed of any inaccuracies or biases. Systems in this stage of the data engineering process use sophisticated pattern recognition frameworks that identify and automatically redact every possible bit of sensitive information – from Social Security details to proprietary code and any other intellectual properties before the data is fed into the model for further preprocessing and training.
Secure Enclaves – Secure enclaves are like the safehouses of the Gen AI world. These comprise completely isolated (and we mean, completely isolated, even from any form of wireless connectivity), in which the AI models process enterprise sensitive data without any possibility of external communication (imagine a panic room bunker, if you will). According to the latest report released by Intel in April 2024, organizations that use the Secure Enclave Methodology for AI processing have witnessed ZERO data leaks across more than 10, 000 deployments, proclaiming secure enclave based AI processing to be the safest enterprise sensitive AI processing methodology.
WHERE YOU COME IN
An uncomfortable truth to begin with: Forrester’s report last year on AI processing safety reported that 82% of all data breaches are a result of human error. In essence, the report says that while we are busy worrying about the latest AI model or the latest sophisticated frameworks potentially leaking our proprietary data and intellectual properties, someone from our own department or even the next cubicle is more likely to click on a link that presents a far bigger threat to Enterprise AI- The simple reason why any serious enterprise level Gen AI project needs for factor in the risk mitigation of the human element as one of the most crucial and prominent. As the adage goes, “the most secure technology system in the world is only as good as the people operating it”. This is where the crucial human element in securing Gen AI for the enterprise comes into play – constantly upgraded skills training and certifications.
Fortunately, the critical and volatile element of humans in handling sensitive enterprise data, especially when dealing with AI systems, has woken up to this challenge, and several prestigious institutions, including the likes of USDSI® now offer comprehensive programs in data science security that encompass a wide range of topics, from model architecture to ethical considerations.
So, dear reader, to ensure that your organization does not end up on the next high value data breach report of the future, get certified, because the future of data science and subsequently that of AI, is secure AI. So go ahead, get certified, apply your knowledge and skills, and keep climbing the rungs of one of the most lucrative careers in the world today.
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