Prioritizing Data Security In The Age Of Generative AI

Uploaded on 2024-08-27 in TECHNOLOGY-Key Areas-Artificial Intelligence, TECHNOLOGY--Resilience, FREE TO VIEW

In today’s rapidly evolving technological landscape, the integration of generative AI brings with it a wave of innovation and potential. From revolutionizing creativity, enhancing productivity and generally helping people and businesses work even more effectively, the implications of technologies powered by AI are vast and far-reaching.

However, amidst the excitement lies a pressing concern: the cybersecurity risks that AI can pose to our data.

As security leaders and their teams embrace generative AI for everything from speeding up administrative tasks to pushing strategy into action, the business imperative to prioritize data security everywhere is clear. Generative AI systems, with their ability to process vast amounts of data, present a double-edged sword for companies. While they offer unprecedented capabilities, they also introduce significant security vulnerabilities. Many tools can collect, store and process large amounts of data from various sources – including user prompts. Consider a scenario where an employee may inadvertently disclose sensitive information (such as unreleased financial statements or intellectual property) through a seemingly innocuous prompt. Tools like ChatGPT or Copilot could leak that proprietary data while answering the prompts of users outside of the organization – not only exposing the organization to third-party risks but also amplifying the potential for data leaks.

The AI Transformation & Data Security

The digital transformation ushered in a new era, but a more profound revolution is upon us: the AI transformation. AI is rapidly reshaping entire industries, and data security is no exception. Businesses must adapt their security strategy to accommodate this, as generative AI risks are revealing themselves as multi-faceted threats that stem from how users inside and out of the organization interact with the tools. And amid all of the opportunities presented by these new technologies, the reality is that AI is further amplifying the need for robust data security controls.

Embracing innovation while alleviating the security risks of generative AI broadly lies in three key pillars: employee awareness, security frameworks, and technological solutions.

1. Employee Education: Educating employees on the safe handling of sensitive information is paramount. It can be easy for employees to overlook or not even think about data risks when using AI tools, so by raising awareness about the risks associated with generative AI and implementing clear guidelines for data usage, businesses can mitigate the likelihood of accidental data exposure. Security leaders must ensure employees understand what information can and can’t be shared with AI tools, while also informing users of the risk in malware and phishing campaigns that can result from generative AI.

2. Security Frameworks: Developing company-wide guidelines or frameworks on working with approved AI tools and eliminating shadow IT usage of AI can help users make the right decisions from the start. At a global level, establishing best practices for AI is a challenge because of how quickly the technology is continuing to develop, but a number of countries are continuing to develop frameworks and guidelines.

3. Prioritizing Data Security Everywhere: Securing data wherever it resides is a business imperative today. Traditional Data Loss Prevention (DLP) capabilities are super powerful for their intended use cases, but with data moving to the cloud and dispersed amongst tools, it is now clear that DLP capabilities also need to evolve while extending abilities and coverage. Cloud-native DLP solutions offer a holistic approach to data security, ensuring compliance and protection across diverse environments. By prioritizing unified enforcement to extend data security across key channels, cloud-native DLP streamlines out-of-the-box compliance and provides enterprises with industry-leading cybersecurity wherever data resides – essential for safeguarding the AI era.

Additionally, leveraging innovations such as Data Security Posture Management (DSPM) solutions further enhance data protection capabilities.

The sheer volume and complexity of data can be overwhelming – but AI-powered DSPM solutions such as Forcepoint DSPM equip businesses with unmatched accuracy to help security leaders identify and remediate data risks proactively, bolstering their security posture in real-time. At Forcepoint, our commitment to data security extends beyond mere protection – it's about empowering organizations to thrive in an era of unprecedented digital transformation.

A Unified Approach

Platforms that combine technologies like DSPM and DLP into a unified solution that prioritizes data security everywhere are ideal – bridging security capabilities wherever data resides. At Forcepoint, our Data-first approach to security offers a comprehensive solution for securing data across all channels – enabling organizations to confidently integrate generative AI with full, real-time visibility and control.

Establishing security frameworks that prioritize data security everywhere is integral for organizations looking to navigate the AI transformation with confidence. The advent of generative AI heralds a new era of innovation and possibility, but, with great power comes great responsibility – and it's incumbent upon businesses to prioritize data security in this evolving landscape.

By embracing a proactive approach to cybersecurity and leveraging innovative solutions, organizations can unlock the full potential of generative AI while safeguarding their most valuable asset – their data.

Samer Diya is META Vice President at Forcepoint

Image: Shubham Dhage

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