Move over GDPR – there’s a new sheriff in town. The world is still reeling from the EU’s recent negotiated agreement over the A.I. Act, solidifying it as one of the world’s first comprehensive attempts to govern the use of AI. Enforcement won’t kick in until 2025, but IT leaders are already trying to stay ahead lest they risk falling behind.
Here’s why IT leaders are trying to pay attention to the new AI regulations coming from the EU.
AI regulations – new IT girl in town
When the EU first introduced GDPR five years ago, the world went through a seismic shift as organizations scrambled to ensure they updated their privacy notice, ensuring transparency on how they govern their data and everything in between. Since then, GDPR has accrued over $4 billion in fines.
With GDPR setting the standard of what data privacy and regulations should look like, the EU sets out to once again change the way we view regulations. Many are hailing this historical moment as a right step in the right direction, with the regulations outlining what tech companies that make AI will have to show or risk incurring fines of up to 7% of their global sales (GDPR fines are up to 4%.)
The EU AI regulations outlined the following:
- Ban social scoring systems
- Regular risk assessments
- Summary of what data was used for training models
- Proof of audits and reporting on the data used
Ban use of bulk scraping of facial images and most emotion recognition systems in workplace and educational settings
Before getting into the nitty gritty details, we have to examine how AI has exploded in 2023 alone.
The rise of AI and why regulation is important
When you take a look at the numbers, AI has quickly become every organization’s newest productivity tool— with a recent study citing that 37% of organizations have implemented some form of AI in their business, a 270% increase from four years prior. Industries across the spectrum are witnessing a surge in AI applications, revolutionizing the way businesses operate.
Whether streamlining operational processes or enhancing customer experiences, AI is reshaping the landscape of work. In fact, 54% of business executives say that AI solutions implemented in their workplaces have already increased productivity. A great example of this is in the healthcare sector, where AI-driven diagnostics are expediting medical assessments and improving patient outcomes.
The financial sector, in particular, has seen significant advancements with AI-driven algorithms making data-driven investment decisions. From chatbots in customer service to predictive analytics in manufacturing, the rise of AI is undeniably reshaping industries.
Risk of AI and the pitfalls
However, the trajectory of this AI revolution is not without its challenges. As organizations increasingly embrace AI, they must grapple with a host of potential pitfalls. One main concern? Privacy.
With AI systems processing vast amounts of personal and sensitive data, the risk of unauthorized access and breaches looms large. Ensuring robust cybersecurity measures is imperative to safeguard against these threats.
Additionally, the ethical implications of AI decision-making algorithms raise critical questions. The potential for bias in algorithms, leading to discriminatory outcomes, poses a significant challenge. As AI systems learn from historical data, they may inadvertently perpetuate existing biases. Recognizing and addressing these ethical concerns is paramount for responsible AI deployment.
Don’t wait until 2025 – Get ahead of the regulations now
While there’s still a while before the new AI regulations kick in, there are steps organizations leaders can take today to ensure that they are adopting AI responsibly including:
- Adopt a forward-thinking data governance strategy – Everyone knows that AI is only as good as the data that it is trained on. However, before feeding that data into various AI systems, utilize a deep data discovery platform to accurately identify, classify, tag, and label it accordingly. Additionally, organizations should have a proverbial data purgatory, an intermediate stage where IT leaders can check their data for any sensitive information and remove it before feeding the AI with it.
- Engage with AI developers and security experts – The worst time to prepare for any potential data breach is after it happens. Ensuring that the AI model you are using doesn’t eventually lead to this breach is no different. Organization leaders need to actively collaborate with experts in the field to stay up to date on the latest ins and outs of the workings and weaknesses of AI systems. Organization leaders can continuously tailor their data management and security systems to the latest threat by staying up to date.
