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Summary

  • Chief AI officers and unstructured data insights are set to become the norm in 2024, as generative AI sweeps through the workplace.
  • Data breaches will also increase due to the rise of shadow AI.
  • Attackers will also pivot away from malware-based attacks to focus on identity compromise in cloud apps.
  • Investing in tools to secure generative AI will be critical for organizations looking to harness the full potential of this revolutionary technology.

2023 was, undoubtedly, generative AI’s breakout year. What began as a niche realm reserved for the tech-savvy elite quickly transformed into a democratic tool accessible to the masses. AI’s ascent has been nothing short of meteoric.

But here’s the burning question: What lies beyond the horizon? While AI rattled the foundations of the workplace in the past year, the reality is that we’ve barely scratched the surface. Brace yourself, because this technology is going to be more revolutionary for the world of work than the dot com boom. Think: new jobs. New industries. And, for cybersecurity professionals, new attack surfaces. 

So, what does 2024 have in store for generative AI and the workplace? 

Here’s our take. 

Organizations will unlock the pitfalls & potential of unstructured data 

Believe your organization has a firm grasp on its data landscape? Think again. Unstructured data, encompassing everything from Slack messages to Facebook posts, currently makes up less than a third of the data managed by businesses. 

But here’s the game-changer: analysts foresee that by 2024, generative AI subsets like natural language processing (NLP) will empower enterprises to double their capacity for handling unstructured information.

For customer success and marketing teams, this heralds a new era of opportunity. Artificial intelligence will equip these departments with the means to unlock a treasure trove of previously impenetrable insights, spot emerging trends and patterns, and make data-driven decisions that elevate their success.

Simultaneously, cybersecurity and compliance teams are poised to harness the powerful capabilities of NLP. This technology promises to revolutionize the way organizations discover, classify, and safeguard unstructured data residing in cloud apps like Teams and Google Workspace.

Previously, traditional data classification tools struggled to identify unstructured information. This is because it lives in so many places–including servers, cloud infrastructure and third-party software–and doesn’t abide by a predefined structure.

For traditional tools, discovering and classifying this information accurately was near impossible, escalating the risks of accidental data breaches and data theft. 

Thankfully, advanced NLP-based tools can effortlessly identify, monitor and track unstructured information, making it much easier for cyber-risk professionals to ensure that sensitive information is only accessed and used by the appropriate people in an appropriate way. 

The rise of the chief AI officer 

Have you ever crossed paths with a CAIO? The acronym stands for Chief AI Officer, a role that is slated to become increasingly prevalent in the corporate landscape in 2024. Intriguingly, research suggests that approximately one-fifth of companies have already embraced the notion of a CAIO to spearhead their overarching AI strategy.

While the precise contours of the CAIO’s role may vary from one organization to another, some common threads are emerging, with the CAIO typically responsible for AI strategy and innovation, risk management and compliance, the orchestration of project implementation and optimization, and adeptly navigating stakeholder relations.

It’s important to note that finding a CAIO doesn’t necessarily entail creating an entirely new executive role. In fact, for organizations already equipped with a data team and a Chief Data Officer (CDO), the CAIO function can be integrated seamlessly into the existing framework.

The decision to employ a CAIO hinges on a multitude of factors, including the size of your organization and its technological maturity. Ideally, you should strive to reach a state of data democratization before investing in a CAIO.

With that said, industry analysts stand firm in their belief that the coming years will demand the presence of an executive leader who can laser-focus on unlocking AI value, not only in corporations but also for SMEs.

Identity compromise becomes the number one threat factor 

With the majority of work now taking place within Software as a Service (SaaS) applications, identity compromise is poised to become the primary modus operandi for malicious actors in 2024.

Rather than focus on zero-day exploits and ransomware, malicious actors will increasingly exploit compromised identities and leverage their associated access privileges–a form of attack that is much more stealthy than malware-based attacks. 

For organizations, the rise in identity-based attacks will trigger a renewed investment in zero trust architecture: tools and policies that deny or grant user access in real-time, contingent upon the user’s behavior and the presence of any suspicious activity. 

Shadow AI triggers a new wave of data breaches  

Right now, 60% of employees are using unauthorized AI tools to boost productivity and efficiency. By unauthorized, we mean they’re using tools that aren’t vetted or controlled by the IT and security departments. 

The trouble with this is that it skyrockets the risk of data leakage and compliance violations. Without visibility into the data employees are sharing with AI tools, security teams can’t block or redact sensitive information from becoming exposed. 

To make matters more concerning, we must remember that input prompts and responses are crucial for refining and training AI models. Consequently, any data entered into these systems has the potential to reappear as an output when another user submits a prompt, meaning a data breach could happen any second–and your organization will be no wiser to it until it’s too late. 

Thankfully, specialist tools like Polymer data loss prevention (DLP) for AI can combat the risks of shadow AI. Our platform, for example, is a plug and play DLP solution for platforms like Chat GPT that prevents sensitive information from being shared as an input or an output, meaning you no longer have to worry about shadow AI.  

2024: Closing the gap between innovation & security

As we look to the year ahead, it’s clear that generative AI will continue to have a huge influence on the world of work. However, to make the most of this burgeoning technology, organizations must balance AI innovation with security and compliance. 

If you don’t know where to start, we’re here to help. Polymer DLP for AI is a low-code solution designed to make securing sensitive data in apps like Chat GPT and Bard seamless and autonomous. Read our whitepaper to find out more.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.

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