Preserving AI Ingenuity Without Compromising Speed – FireTail Blog
AI protection is a pivotal concern in today’s setting. While developers, groups, staff, and business sectors are swiftly advancing to vie with each other, security units consistently lag in an environment where new hazards emerge daily.
AI protection is a pivotal concern in today’s setting. While developers, groups, staff, and business sectors are swiftly advancing to vie with each other, security units consistently lag in an environment where new hazards emerge daily. Consequently, there has been a notable increase in AI breaches in 2025.
As indicated by Capgemini, 97% of enterprises encountered incidents linked to generative AI endeavors in the preceding year.
It is uncertain whether all these incidents were breaches, or if some were simply vulnerabilities; nonetheless, about half of these enterprises revealed that the financial impact could surpass $50M per occurrence. This illustrates the magnitude of the data involved, as well as how each incident might indicate a fundamental flaw, potentially exposing an entire dataset.
So how can developers and security teams collaborate to keep innovating in the realm of AI without compromising security? The dilemma is intricate and necessitates a multifaceted approach.
From Code to Cloud…
One effective way to assure the security of your AI is to commence during the design phase. At FireTail, we frequently discuss safeguarding your cyber assets from “code to cloud.” Designing your models with security in mind enables you to stay ahead of threats rather than needing to consistently react to emerging risks.
Emphasizing security from code to cloud is imperative.
Development units and security teams must collaborate during the design phase to ensure the mutual prosperity of both parties. We have previously addressed the widening gap between developers and security teams, but to establish a comprehensive security stance, this gap must be bridged right from the outset by involving security teams in the initial stages of design and development.
Visibility – If you can’t observe it, you can’t shield it.
It is fundamental knowledge that visibility and discovery are the foundation of a robust cybersecurity stance. Complete visibility enables security teams to stay ahead of threats by identifying vulnerabilities and misconfigurations before they surface.
Every member of your team should be aware of the AI models in use, their purposes, the type of information that can be inputted into them, and what is forbidden, among other aspects. Security units should be diligent in monitoring AI operations and actions. A centralized dashboard can streamline the monitoring of these operations, ensuring nothing escapes notice.
Surveillance
A robust AI security stance should involve continual monitoring to track alterations. The application scenarios for AI evolve over time, owing to new technologies, etc., so it is vital to be on top of which models serve what functions within your team and what data they are fed. Visibility is just the initial step in overseeing your AI usage and interactions. Yet, with consistent monitoring and warning mechanisms, both security teams and developers can discern real-time changes and swiftly respond, remaining ahead of threats.
The Hurdles of AI Logging
AI logging is one of the most substantial challenges for AI security. One reason for this is that new AI providers frequently establish fresh log formats based on their own LLMs. Security teams may strive to learn about and comprehend their known LLMs, but each time they adopt a new model, they essentially have to relearn the process, dampening the pace of innovation.
As laborious as it might appear, the sole approach to keep up with logging is to log each LLM individually to evade errors and verify the accuracy of each log before proceeding. Prioritizing precision over efficiency may seem counterintuitive, but ultimately, if teams neglect adhering to proper logging protocols, they will end up expending more effort rectifying errors, spending as much time as they would have by meticulously carrying it out correctly in the initial instance.
Regulatory Compliance
Many organizations hasten to test AI with their proprietary data, but some of this data may be subject to regulatory compliance prerequisites. Consequently, sharing this data with third-parties, like an LLM, might necessitate user consent.
Rules, such as GDPR and CCPA (California Consumer Protection Act), specify terms regarding issues like data sharing that developers may not realize they are subjected to until it’s too late. Frequently, specific criteria slip through the cracks when listed in minuscule print, leading to no immediate repercussions.
So, how do you ensure compliance with regulations perpetually updating and evolving? The most effective technique to guarantee compliance involves continuous monitoring of your landscape and every interaction as you progress. It may seem arduous, but it is the sole foolproof way to avert repercussions.
The OWASP Top Ten for LLM
The OWASP Top Ten Risk List for LLMs was curated by AI security experts based on a grounded understanding of threats and vulnerabilities. This list provides details and mitigation tactics for the ten most pressing risks to LLMs nowadays, spanning from input injection to disclosure of sensitive information, and more.
The OWASP LLM Top Ten can function as a risk gauge for teams to evaluate their LLMs against.
While the OWASP Top Ten list is comprehensive, it is not exhaustive or a blueprint for teams to adhere to. Nonetheless, it serves as an excellent starting point for developers and security teams alike to educate themselves on the prevalent risks in the ecosystem and how to shield against them.
FireTail
FireTail’s AI security platform can aid developers and security teams in remaining one step ahead of threats. FireTail provides a centralized dashboard for monitoring all your AI operations and activities, along with your API endpoints and more to sustain your awareness and exploration from the design phase onward. To explore how it operates, schedule a demo or trial the platform for free, here.
*** This content is syndicated from FireTail – AI and API Security Blog authored by FireTail – AI and API Security Blog. Access the original post at: https://www.firetail.ai/blog/securing-ai-innovation-without-sacrificing-pace
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