In this episode, we unpack one of the most overlooked but dangerous risks in AI deployment—insider threats. While organizations often focus on securing data at rest and in transit, there's a blind spot few talk about: data in use.
Imagine a secure, on-prem AI system running in your data center. It sounds safe—but what if a trusted insider with just enough access could dump memory and expose raw, unencrypted sensitive data?
For industries like finance and healthcare, where data privacy is mission-critical, this is a nightmare scenario.
We dive into real-world concerns from companies handling PII, financial transactions, and medical records. They’re skeptical of SaaS AI and even cautious about internal data sharing.
So what’s the fix? It's not just about where AI runs—it's how it's built.
This episode explores why Confidential Computing is critical for truly secure AI. From in-memory encryption to secure enclaves and built-in guardrails, we discuss what a next-gen AI platform must include to defend against insider misuse and keep data secure through every stage of processing.
If you're responsible for AI security or data governance, this episode is your wake-up call.
In this thought-provoking episode, we explore a major obstacle standing in the way of AI innovation: the complete lack of an enterprise-grade AI platform that meets the unique demands of high-trust industries.
From banking and intelligence to healthcare, organizations are eager to deploy AI—but only if it guarantees the highest levels of privacy, security, and compliance.
We walk through real-world use cases—a top global bank, a national intelligence agency, and a major healthcare network—all of whom could benefit immensely from AI, yet remain stuck due to the absence of secure, controllable infrastructure.
Their needs go beyond what today’s AI tools offer. No public cloud API, generic enterprise setting, or patched-together framework can meet their standards for data sovereignty, zero-trust architecture, offline operability, or air-gapped deployment.
This episode dives into why today’s AI offerings fall short, what’s really needed to unlock the most sensitive and high-value use cases, and what it will take to build a trustworthy AI foundation for the future.
If your organization handles sensitive data and is serious about secure AI, this conversation is essential.
In this episode, we tackle one of the most pressing questions in today’s AI-driven world: Who’s responsible when generative AI gets it wrong?
As enterprises increasingly adopt GenAI for productivity, content creation, and analytics, the stakes rise just as fast. But with those benefits come real challenges—AI hallucinations, misinformation, data privacy breaches, and regulatory risks.
We dive into the rising concerns surrounding AI-generated falsehoods and the legal, ethical, and reputational fallout for businesses.
Who should be held accountable—CISOs, compliance officers, AI developers, or executive leadership? The truth is, responsibility is shared—and avoiding risk means building strong governance from the ground up.
This episode explores the urgent need for AI accountability frameworks, Zero Trust principles in AI deployments, and the role of advanced platforms in securing data, governing models, and preventing harmful outputs.
If you're wondering how to use GenAI safely and responsibly—this conversation is a must-listen and check out the Zero Trust AI platform for secure and compliant GenAI deployments.
In this episode, we examine one of the most significant legislative frameworks shaping the future of artificial intelligence—the EU AI Act.
As AI continues to transform industries and daily life at an unprecedented pace, the European Union is leading the charge in regulating its development and use through a risk-based, human-centered approach.
We explore how the Act categorizes AI systems into Unacceptable, High, and Low Risk, what this means for industries like healthcare, finance, and law enforcement, and the serious compliance requirements that high-risk AI systems must meet.
The episode also unpacks regulations on general-purpose AI models, the enforcement of prohibited practices like social scoring, and how the EU aims to balance innovation with ethical responsibility.
Join us as we explain why the EU AI Act is more than just regional regulation—it's setting the global benchmark for responsible and transparent AI governance. Check out the AI governance and risk mitigation platform for high-stakes use cases.
As AI adoption accelerates across industries, so do the risks and regulatory concerns. In this episode, we deeply dive into AI governance—what it is, why it matters, and how organizations can implement it to ensure ethical, compliant, and trustworthy AI systems.
We unpack the real-world challenges businesses face when deploying AI, especially when sensitive personal data is involved, and explain how governance frameworks can mitigate risks like bias, privacy violations, and legal exposure.
This episode offers actionable ideas for building responsible AI training, core ethics, leveraging automated monitoring tools, and right committee call principles strategies, from setting up overnights for leads.
Key takeaways include:
What AI governance really means and why it’s a strategic imperative
The role of governance in minimizing legal, reputational, and operational risk
Multi-layered governance: organizational, regulatory, and technical perspectives
Best practices for setting up AI governance frameworks that scale
How to stay ahead of evolving global regulations like the EU AI Act and SR-11-7
Why governance is essential to building public trust and sustainable AI systems
Whether you're an executive, compliance officer, or AI practitioner, this episode will equip you with the knowledge to steer your AI initiatives with confidence and accountability.
Discover how Fortanix Armet AI enables secure and compliant AI deployment with built-in governance, transparency, and control.
In this episode, we explore how large language models (LLMs) have human-computer interaction and revolutionized why they're not without limitations.
While LLMs can generate impressively human-like responses, they often rely on static training data, leading to outdated or inaccurate answers that may erode user trust.
To address these challenges, we dive into the powerful technique of Retrieval-Augmented Generation (RAG).
Learn how RAG enhances LLMs by combining their generative abilities with real-time, reliable data sources—resulting in more accurate, up-to-date, and trustworthy AI outputs.
We break down:
- How Retrieval-Augmented Generation works
- Why semantic search is critical in this process
- The cost and control advantages of RAG for enterprises
- Best practices for implementing RAG in real-world systems
Whether you’re an AI developer, tech leader, or simply curious about the future of generative AI, this episode gives you the tools to understand how to make AI work smarter, not harder.
Fortanix product managers encountered challenges in overhauling their pricing model due to scattered and unstructured data residing in various systems. They envisioned using AI to extract insights by querying this data with natural language.
However, concerns about data security and confidentiality arose with public AI models. This led to the concept of an on-premise AI solution that keeps sensitive data secure, allows LLM management, respects access policies, offers easy data ingestion, and is user-friendly for executives.
Organizations are increasingly integrating generative AI, but this adoption introduces significant security, privacy, and regulatory concerns.
OWASP has identified the top ten security risks for large language models in 2025 to guide enterprises in mitigating these challenges.
These risks range from prompt injection and sensitive information disclosure to supply chain vulnerabilities and misinformation.
For each identified risk, the source provides a brief explanation, an illustrative example, and several high-level mitigation strategies. The goal of this information is to help businesses build secure and compliant generative AI applications.
A follow-up series will offer more in-depth analysis and best practices for addressing these critical vulnerabilities.
Rapid AI adoption presents significant security challenges, as these intelligent systems learn from, store, and potentially leak sensitive data.
A recent GenAI report highlights that a large majority of organizations have already experienced data breaches, indicating current security measures are insufficient for AI environments.
This crisis is fueled by the exposure of sensitive data in AI models, the uncontrolled use of "Shadow AI" by employees, and the inadequacy of traditional security approaches.
To address these vulnerabilities, organizations must adopt a data-centric security strategy embedded throughout the AI lifecycle, foster collaboration between IT and security teams, and invest in AI-specific security solutions to build resilience against inevitable breaches. Ultimately, integrating robust security measures is crucial for enabling sustainable AI innovation and reducing risk exposure.
