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Do You Really Need Another Security Product?!

The combination of booming security tools and alerts and security talent shortage, might lead to the effort of security tools consolidation. While this is true for most of security endeavours, AI introduced new risks and challenges that cannot be met by the existing stack.

Tomer Avni 2 May 2024 6 min read

The Cybersecurity Conundrum: More Tools and Alerts, Less Talent

In an era where the digital landscape is expanding at an exponential rate, the role of the security leader has become extraordinarily complex. Innovation isn’t just growing; it’s exploding, pushing businesses into uncharted territories and bringing forth challenges that were once mere figments of the imagination. Just think a typical Fortune500, with tens of security tools, to portray the challenge at hand.

Furthermore, this adds layers to an already intricate situation, with the scarcity of human capital in cybersecurity. The hunt for top-tier cybersecurity talent is in full swing, signaling the sector’s urgent need for human capital to configure new and existing tools, go through the infinite number of alerts, and fix what needs to be fixed.

This proliferation of tools has led to an undeniable truth: the path forward isn’t about acquiring more security products but rather maximizing the potential of what already exists. It’s about streamlining, consolidating, and optimizing to ensure efficacy. Yet, as straightforward as that may seem, there’s a catch—a seismic shift is reshaping everything we thought we knew.

AI: The Tectonic Shift Reshaping Business and Security

History reminds us that transformative technologies like PCs, the internet, and cloud have redefined the business landscape. Today, Artificial Intelligence (AI) stands as that tectonic shift, leaving indelible marks on corporate boards, business investments, strategic planning, and even the buzz on the street. In the realm of natural selection within the digital ecosystem, adopting artificial intelligence is not merely an advantage—it is an imperative.

Generative AI (GenAI) technologies, in particular, are setting the stage for an epic showdown among tech giants and igniting a gold rush for entrepreneurs. Fortune 500 companies are not just taking notice; over 92% are actively integrating GenAI into their operations. It’s a race to harness GenAI’s potential for enhancing business missions and empowering employees to reach unprecedented heights.

But with great power comes great responsibility. GenAI is opening Pandora’s box, unleashing a complex maze of security, privacy, and compliance dilemmas. It begs the question—can our current security solutions keep up?

The Inadequacy of Traditional Security in an AI-Powered Age

The risks GenAI poses are tangible, yet existing security solutions seem ill-equipped for the task. One might assume that the buzzwords of “AI security” touted by vendors would suffice, but here’s the stark reality: Traditional tools fall short in addressing the newly introduced AI security challenges.

Take, as an example, the first solutions that come to mind – Data Loss Prevention (DLP) and Cloud Access Security Broker (CASB). These tools focus on the data leaving your organization and blocking its flow if it violates your pre defined policies. GenAI demands far more nuanced management over all data sharing configurations and a deeper understanding of content, context, and the intricacies of AI interactions:

  • GenAI Misconfiguration : Each GenAI service (for example, chatGPT) has specific configurations (for example, “train with my data”), which requires more granularity to know whether specific data sharing is allowed or not. Some misconfigurations are intricate and hard to detect . For example, Microsoft 365 Copilot might build its context by accessing data without the users knowledge, leading to potential data leakage that is hard to detect.
  • Beyond the Browser: The Reach of Data Risks: data leakage or exposure risks go beyond your browser. They might occur via APIs, applications used by employees (like GitHub Copilot), or applications you built and used by your customers. These go beyond the reach of traditional DLP or CASB tools.
  • From Prevention to Enablement: In 2024, the old-school approach of prevention is giving way to enablement. Blocking data flows can inadvertently block business progress, emphasizing the need for a more sophisticated balance.

Stepping back to see the bigger picture, AI security transcends outbound data leakage; it encompasses the detection and remediation of unsupervised outputs, AI exploits, and intricate access management within GenAI ecosystems. To tackle this, we need solutions that:

  • Monitor data and code flow across GenAI models, infrastructures, and applications, including internal file systems and code repositories.
  • Grasp the complexities of GenAI sessions, such as conversation topics, sentiment, and the criticality of discussions (are they about decision making?). As such, rigid pre defined rules wouldn’t be able to detect complex prompt injection attacks, but only solutions that have deep understanding of the context.
  • Oversee all interfaces—web, API, apps—to detect, correlate, and remediate malicious activities and outputs – because AI is everywhere.

Builders will Build

In the rush to harness the transformative power of AI, it’s tempting to look to the titans of technology—OpenAI, Microsoft, Google, and Amazon—to safeguard the future of this burgeoning field. Their role in shaping a secure AI landscape is crucial, but it’s a mammoth challenge that requires a joint effort:

  • The Builders’ Dilemma: Innovation vs. Security: At the heart of innovation, these builders charge forward with one primary objective: to deliver cutting-edge functionalities and an unparalleled user experience. In the fiercely competitive AI arena, speed is of the essence, and security often takes a back seat. It’s a familiar narrative, echoing past transitions with PCs and cloud technologies, where security considerations followed rather than led the charge.
  • The Call for an Independent Sentinel: Just as history teaches us, securing a digital enterprise of such magnitude requires an independent security layer—a vigilant guardian dedicated solely to protection. The AI sector has yet to adopt a shared responsibility model, but it’s anticipated. Vendors and customers alike must unite, each bearing a portion of the protective mantle, with an independent AI security solution serving as the keystone.
  • Cross-Platform Cohesion: A Unified Defensive Front: Builders, with their inward focus, naturally try to secure their own offerings. They won’t integrate, monitor, and remediate AI activities and risks across diverse platforms. A singular, overarching solution is necessary—one that can enforce consistent policies and offer a unified view of security across all AI platforms.

Laying the Foundation for AI Security

As AI rapidly becomes the pulsing core of modern enterprises, establishing a robust AI security foundation is not a future consideration—it’s an immediate imperative. With new threats emerging as swiftly as AI itself evolves, the time to act is now.

In your search for an AI security solution, consider who is truly immersed in the ever-evolving landscape—someone who understands the shifting sands of the attack surface, keeps abreast of regulatory changes, and stays on top of the myriad functionalities and interfaces that builders release daily. Your choice should be a partner who lives and breathes AI security, prepared to face the challenges of today and tomorrow – that’s your new security product.

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