*Per Marco Tadeu
Exactly 160 years ago, the novelist Samuel Butler stated that Darwinian evolution can apply to machines and that one day they could become conscious. Almost 75 years ago, legendary mathematician Alan Turing created his well-known Turing Test to measure machine intelligence. Twenty years ago, explorer robots NASA's Spirit and Opportunity they were crossing the Martian surface, autonomously avoiding obstacles with the help of artificial intelligence (AI).
And now, the North American company OpenAI launched ChatGPT. With a simple online or mobile app chat box user interface available to even the tech newbies among us, it's perhaps the most advanced and accessible AI ever. Do you need an essay on quantum computing? Just ask ChatGPT. Do you need to know how to arrange flowers for a bouquet? Just ask ChatGPT.
ChatGPT, along with contemporaries like GoogleBard, has sparked an unprecedented furor over AI with much excitement over the technology's nearly limitless potential for good.
The challenges presented by AI
But there is another side to the story. After all, Samuel Butler's suggestion that Darwinian evolution might apply to machines eventually led him to conclude that machines might eventually supplant humanity. And more recently, some of the greatest minds of our time have advocated pausing the AI development until society can implement a set of safety protocols.
Indeed, AI is a double-edged sword of data management – just as it has enormous benefits, it can also create potentially significant problems. For example:
- Cybercrime
It's not out of the question that AI chatbots like ChatGPT and the technology behind them could usher in a golden age of cybercrime, where even the least technical criminals turn to stealing data for profit. Do you need ransomware? Just ask. Or advanced cybercriminals can use these tools to create even more innovative attack vectors and malware. Need a more sophisticated phishing scam? Just ask.
- Volume, Velocity and Variety of data
Consider the three Vs of data: volume, the total amount of data your company is creating; velocity, the rate at which your company creates this data; and variety, the number of formats in which the data comes. When you remove everything else, AI is really just taking in existing data, “thinking” about that data, and then generating even more data faster and in a wider variety of formats.
Thus, AI can also increase the challenge of managing today's already vast amounts of data spread across on-premises and multi-cloud infrastructures that enterprises are dealing with, which has security and cost implications. And with the world's data – which is stored in data centers largely powered by fossil fuels – already generating as much carbon waste as the entire airline industry, the potentially overwhelming amount of unnecessary data generated by AI could also have a significant environmental impact.
- Compliance and data governance
Additionally, AI tools like ChatGPT create compliance and data governance challenges. Employees are turning to them to simplify their jobs. While this can improve productivity, there is a huge risk when employees share sensitive information, such as regulatory documents, earnings reports or personally identifiable information, with chatbots. AI can even use this information as part of its machine learning process to inform the answers it provides to others.
Overcoming some of the challenges of AI with the help of… AI
But make no mistake: I'm optimistic about the future of AI and the impact it can have on our lives, especially data management in an increasingly multi-cloud world. In fact, I believe that AI-driven data management that does what IT teams can't or doesn't have time to do is the answer to many of the challenges that the broader application of AI creates. Here are just a few ways that AI-powered data management can solve some of the potential problems that AI itself can create.
- Dynamic Cyber Resilience
The threat landscape was evolving at an alarming rate even before generative AI tools arrived on the scene, but now it can accelerate to unimaginable levels. The solution may be to fight fire with fire. Adopting AI-driven anomaly detection and other similar security measures as part of a comprehensive data management strategy can help companies protect themselves against the effects of the constantly evolving threat landscape.
- Data services that automatically provision, optimize and recover
Data management leaders need to continue developing services that can autonomously self-provision, self-heal, and self-optimize, including advanced autonomous deduplication. This may help explain the vast amounts of data in today's multi-cloud environments, which will certainly only increase as generative AI increases the volume, velocity, and variety of data created. As I stated earlier: In practice, this will look like autonomously provisioning data protection policies when new services and users are deployed, and autonomously monitoring and deploying new policies that match the observed usage of a company's data – again, everything no human decision making required.
- Standardized Regulations
AI is already playing a role in advanced compliance and data governance solutions, such as staying ahead of potential compliance issues across an unprecedented number of business communications platforms, and could eventually play a role in helping prevent leakage data through generative AI tools. . But first, we must accept, defend, and implement regulatory limitations designed to keep potentially sensitive information out of these tools. Until these regulations are in place, business leaders can join a growing list of companies implementing their own rules to prevent data compliance issues from arising as a result of employees' use of generative AI tools.
In short, for all their benefits, generative AI tools also present unique challenges, especially when it comes to data management. But AI itself, along with appropriate regulatory constraints, can help offset these challenges.
*Marcos Tadeu, senior manager and sales engineering at Veritas Technologies in Brazil
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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