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Reprinted from Spiceworks

Learn how Trust Layers defend against evolving threats, ensuring a secure AI landscape.

Key issues that have accompanied its meteoric popularity include the need for robust AI security solutions, specifically data and access controls; cost management for the deployment of AI-driven tools; a surge in cyberattacks using large language models (LLMs); and the growing prominence of both multimodal models and small models, says Neil Serebryany, founder and CEO of CalypsoAI.


Neil Serebryany, Founder and CEO, CalypsoAI

AI’s Remarkable Impact on Industries and Security

Artificial intelligence (AI) is arguably the most disruptive and transformative technology in a quarter-century rife with disruptive and transformative technologies. AI-driven tools are changing the topography of the digital world, introducing operational efficiencies across existing industries and creating new industries, full stop. It will not be long before their use is as ubiquitous in modern life as smartphones.

As the enterprise landscape adapts to full engagement with AI-driven tools, such as large language models (LLMs), and developers adapt the tools to fill needs organizations don’t even know they have, their diffusion and acceptance will reach certain milestones, each of which has significant corresponding security repercussions. I believe a few milestones will be met in the coming year.

1. As foundational models grow, so does the need for heightened AI security

Deploying LLMs and other AI-dependent tools across an organization unquestionably brings efficiencies and innovation. Still, it also fosters tech sprawl, an alarming diminishment of observability, and, eventually, flat-out tech fatigue. All of these lead to an inadvertent laxity in organizational security protocols, which renders the system vulnerable to AI-related novel threats. These include prompt injections, data poisoning, and other adversarial attacks against which traditional security infrastructure solutions are helpless. Establishing a security perimeter that acts as a weightless “trust layer” between the system in which the users operate and external models allows security teams full visibility into and across all models on the system. This gives them the agility to identify, analyze, and respond to threats in real-time, protecting the system, the users, and the organization.

While playing an important role in a defense-in-depth strategy, a model-agnostic trust layer is more than just a defense. It can provide proactive, offensive capabilities, such as policy-based access controls that regulate access to the models and rate limits that deter large-volume prompt attacks intended to overwhelm model operability. A trust layer can also support and enforce company acceptable-use policies, ensure compliance with industry norms or government regulations, prevent prompts containing proprietary data from being sent to the model, and block model responses that contain malicious code.

2. Security solutions play a major role in cost discussion

As AI tools are increasingly integrated into daily operations across numerous industries and domains, organizations must pivot to managing and optimizing the costs and return on investment (ROI) of AI deployment at scale. Still, they must do so with a security-first mindset. As the technology matures and organizations move from experimentation or pilot phases into production-oriented deployments, they face a growing need to justify associated costs and prove value while considering the expanded attack surface. Production deployments often require significant human resources, including data scientists and engineers, compute resources, data at the outset, and maintenance, including retraining, to remain relevant long-term. Understanding the costs, which include monitoring and tracking model usage and efficiently allocating resources, enables organizations to make informed decisions and hone their competitive edge while remaining secure.

3. The frequency of cyberattacks using LLMs is rising

Just as LLMs can be used to generate or refine legitimate content, such as emails or source code, they can just as easily be used to generate that content’s digital evil twins. Phishing emails, for example, have become so sophisticated that they can accurately mimic a person’s writing style and “voice,” including idiosyncrasies, which makes them exponentially more dangerous in that the telltale signs of a fake are less discernible to the human eye. Malicious code can be included in emails generated by LLMs and included in responses to queries made to the models themselves; if a security solution is not filtering for the specific language the code is written in, the code will not trigger any quarantine actions and can infiltrate the system with ease. Malicious commands to bypass controls or execute upon the user taking a standard action can be buried in image and audio clips in chatbots that intentionally invite or induce the user to take the action that will trigger the command. The latest addition to the dark arts within AI is the emergence of “dark bots,” or LLMs, developed specifically for malicious activity. These include WormGPT, FraudGPT, and Masterkey, the latter of which has been trained on both successful and unsuccessful prompt injections to enable it to create attacks customized for the target model. This unchecked innovation can stretch the ability of security teams to prevent breaches.

4. Smaller, multimodal models rise, boosting the need for risk management solutions

Large foundation models that began life as LLMs, such as ChatGPT, were unimodal and text-based, generating human-like written content, including translations. Now, just a little more than a year later, many large models, including ChatGPT, are multimodal, meaning the input and/or the output can be different modalities, such as text, audio, images, code, etc. These models are referred to as large multimodal models (LMMs), multimodal large language models (MLLMs), and, more often and more generically, generative AI (GenAI) models. Whatever they are called, their ease of use, capacity for multi-channel creativity, and unlimited potential are making them increasingly popular. But model development innovations have also moved in the opposite direction to spawn a burgeoning variety of small models that offer greater agility, focused utility, and more transparency. As the quantity of resources it takes to create language models decreases, organizations of all sizes develop in-house models trained on proprietary data or deploy commercially developed small language models (SMLs), such as Microsoft’s Orca2 or Google’s BERT Mini.

However, any increase in model usage, irrespective of size or type—large, small, multimodal, fine-tuned, or proprietary—expands the organization’s attack surface and increases the organization’s risk exposure. Security solutions that can sync up to accommodate the scope and scale of a newly expansive model environment are critical tools to meet and defeat the threats. Trust-layer solutions, particularly, will dominate that market.

We expect 2024 to be a pivotal year within the AI security space, with the coming months full of breathless anticipation and wide-eyed wonder as research, enterprise adoption, and AI risk trajectories continue to intersect in unforeseen ways.



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Neil Serebryany

Neil Serebryany

Founder And Chief Executive Officer, CalypsoAI

Neil Serebryany is the founder and Chief Executive Officer of CalypsoAI. Neil has led industry-defining innovations throughout his career. Before founding CalypsoAI, Neil was one of the world’s youngest venture capital investors at Jump Investors. Neil has started and successfully managed several previous ventures and conducted reinforcement learning research at the University of South California. Neil has been awarded multiple patents in adversarial machine learning.