Guardians of AI: Caleb Sima Of CSA On How AI Leaders Are Keeping AI Safe, Ethical, Responsible, and True
Real-time AI Behavior Monitoring We need systems that can monitor AI operations at machine speed. Traditional logging and monitoring won’t work when AI can make thousands of decisions per second. I’ve seen firsthand how quickly AI systems can go off the rails without proper monitoring.
As AI technology rapidly advances, ensuring its responsible development and deployment has become more critical than ever. How are today’s AI leaders addressing safety, fairness, and accountability in AI systems? What practices are they implementing to maintain transparency and align AI with human values? To address these questions, we had the pleasure of interviewing Caleb Sima.
Caleb serves as the Chair of CSA AI Security Initiative. Prior Caleb served as Chief Security Officer at Robinhood where he focused on keeping customers safe. Prior to Robinhood he was Security CTO at Databricks a leading data analytics and machine learning company where he built the security team from the ground up. Previously he was a Managing VP at CapitalOne, where he spearheaded many of their security initiatives. Prior to CapitalOne, Caleb was CEO of Armorize which was acquired by Proofpoint. He also founded SPI Dynamics and BlueBox security, which were acquired by HP and Lookout. He is attributed as one of the pioneers of application security and holds multiple patents in the space and is also the author of Web Hacking Exposed. He serves as an advisor, investor, and board member for security companies.
Thank you so much for your time! I know that you are a very busy person. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I got started in tech and security pretty young — I was that kid who was always taking things apart and figuring out how they worked. By 15, I was reverse engineering software and identifying vulnerabilities. This led me to join Internet Security Systems’ X-Force research team, where I really cut my teeth in cybersecurity. In 2000, I founded SPI Dynamics, which pioneered web application security testing and was eventually acquired by HP. Since then, I’ve founded multiple security companies, served as CSO at Robinhood and Databricks, and now I’m focused on ensuring AI systems are secure and safe as we enter this new era of technology.
None of us can achieve success without some help along the way. Is there a particular person who you are grateful for, who helped get you to where you are? Can you share a story?
I have to mention Ben Horowitz. When I was an EIR at Andreessen Horowitz, Ben became more than just a mentor — he showed me how to think bigger about building companies and making impact. There was this moment during my time there when I was pitching what I thought was a solid idea, and Ben challenged me to think 10x bigger. He said something like ‘You’re solving a problem, but are you solving THE problem?’ That completely shifted my perspective on how to approach building companies and products. His guidance helped shape not just my business acumen but also my approach to leadership.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
First, I’d say my ability to see through complexity to the root of problems. In security and AI, there’s often a lot of noise and superficial solutions. I’ve always been able to cut through that to identify the real issues that need solving.
Second, my drive to take action. I’ve never been one to just talk about ideas — I believe in moving fast and making things happen. When I see a problem that needs solving, I don’t wait for permission or perfect conditions. I love the quote “ask for forgiveness rather than permission”.
Third, my commitment to authenticity. In this industry, especially now with AI, there’s a lot of hype and buzzwords. I’ve always believed in being direct and honest about both capabilities and limitations. This has helped me build trust with teams and customers throughout my career.
Thank you for all that. Let’s now turn to the main focus of our discussion about how AI leaders are keeping AI safe and responsible. To begin, can you list three things that most excite you about the current state of the AI industry?
First, I’m excited about AI’s potential to augment human capabilities in security. We’re seeing AI systems that can analyze threats and vulnerabilities at a scale humans simply can’t match. Second, the democratization of AI tools is fascinating — we’re watching developers and security researchers use AI in ways we never imagined. Third, I’m particularly intrigued by how AI is forcing us to rethink fundamental security principles — it’s like the early days of web security all over again, but at a much larger scale.
Conversely, can you tell us three things that most concern you about the industry? What must be done to alleviate those concerns?
First, we’re rapidly approaching a reality where autonomous AI agents will operate within our corporate environments just like human users — using desktops, accessing systems, and making decisions. This isn’t science fiction anymore; it’s probably within the next year. The security implications are massive. How do we implement zero trust when the ‘user’ is an AI? How do we monitor and control these agents when they’re operating at machine speed? Our current security models just aren’t built for this.
Second, we have a critical problem with AI model auditability and intent verification. When a human writes code or takes an action, we can trace it back, understand their intent, and determine if it was malicious. But with AI models, especially as we move toward autonomous agents making decisions and taking actions, how do we differentiate between malicious and non-malicious intent? The models are essentially black boxes, and we don’t have good tools or frameworks for this kind of auditing yet.
Third, and this is fundamental — we’re trying to transfer human roles to AI systems without the key guardrails that keep humans in check. For humans, identity and accountability are often the only things preventing bad decisions. If I do something malicious, there are consequences because my identity is tied to my actions. But how does this work for AI? We don’t have equivalent systems of identity, accountability, or consequences for AI agents. This becomes especially critical as AI systems take on more responsibility and authority in our organizations. We need to completely rethink our approach to these foundational security concepts.
Many people are worried about the potential for AI to harm humans. What must be done to ensure that AI stays safe?
Preventing AI harm to humans isn’t just about limiting capabilities — it’s about building comprehensive safety systems from the ground up. Think about it like security in autonomous vehicles: you need multiple layers of safeguards, real-time monitoring, and kill switches.
First, we need robust constraint systems — AI systems should have clear, unchangeable boundaries about what actions they can and cannot take, especially when those actions could impact human safety. These constraints need to be hardcoded at the architecture level, not just added as policy layers.
Second, we need real-time oversight systems that can detect and prevent potential harmful actions before they occur. This means developing sophisticated monitoring systems that can understand context and intent, not just track actions.
Third, and this is critical — we need decentralized control systems. No single AI system should have unchecked power to make decisions that could impact human safety. We need to implement structural safeguards, like requiring multiple independent AI systems to validate and approve high-risk actions, similar to how we handle nuclear launch codes.
Finally, we need human-in-the-loop systems for critical decisions. I know this might slow things down, but for actions that could potentially harm humans, we need human oversight and the ability to quickly intervene.
Despite huge advances, AIs still confidently hallucinate, giving incorrect answers. In addition, AIs will produce incorrect results if they are trained on untrue or biased information. What can be done to ensure that AI produces accurate and transparent results?
Everyone’s focused on training data quality, which is important, but that’s just one piece. We need to fundamentally change how we validate AI outputs. Approaches that combine traditional testing methodologies with new AI-specific validation frameworks. I expect we will see more of this as agentic systems evolve.
The key is developing systems that can verify AI outputs against known-good baselines while also detecting novel or unexpected behaviors. Think of it like having an IDS for AI systems — we need to be able to detect when an AI model is operating outside its expected parameters or producing potentially harmful results.
Based on your experience and success, what are your “Five Things Needed to Keep AI Safe, Ethical, Responsible, and True”?
1. Real-time AI Behavior Monitoring We need systems that can monitor AI operations at machine speed. Traditional logging and monitoring won’t work when AI can make thousands of decisions per second. I’ve seen firsthand how quickly AI systems can go off the rails without proper monitoring.
2. AI-Specific Identity and Access Management We need to completely rethink identity for AI systems. This isn’t just about API keys — we need sophisticated systems that can manage AI agent identities, permissions, and interactions with both human and AI systems.
3. Automated Security Testing Frameworks We need automated frameworks that can continually test AI systems for security vulnerabilities, bias, and potential misuse. Traditional penetration testing methods won’t scale for AI systems.
4. Decision Validation Infrastructure We need systems that can validate AI decisions in real-time, particularly for high-stakes operations. This includes both technical validation and ethical checks.
5. Accountable AI Architecture We need to build accountability into AI systems from the ground up. This means creating architectures where every AI decision can be traced, understood, and if necessary, rolled back.
Looking ahead, what changes do you hope to see in industry-wide AI governance over the next decade?
Over the next decade, we need to move beyond voluntary guidelines to established security frameworks specifically designed for AI systems. Traditional governance models move too slowly for AI’s pace of development. I envision something more like an adaptive framework that can evolve as quickly as the technology does.
We need real-time governance systems that can automatically monitor and enforce security standards across AI deployments. More importantly, I’d like to see the establishment of industry-wide AI security testing standards. Right now, everyone’s building their own security approaches in silos. We need standardized ways to validate AI system safety and security, similar to how we developed web application security testing standards in the early 2000s.
What do you think will be the biggest challenge for AI over the next decade, and how should the industry prepare?
The biggest challenge will be maintaining control and security as AI systems become more autonomous and interconnected. We’re moving toward a world where AI systems will be making high-stakes decisions and interacting with other AI systems at machine speed. The real challenge isn’t just about securing individual AI systems — it’s about securing the entire ecosystem of AI interactions.
The industry needs to prepare by building robust security and control systems now, before these autonomous AI networks become reality. We need to develop sophisticated monitoring systems that can track and understand AI-to-AI interactions, identify potential security risks, and intervene when necessary.
You are a person of great influence. If you could inspire a movement that would bring the most good to the most people, what would that be? You never know what your idea can trigger. 🙂
Although this is an AI and security focused discussion, my movement would actually focus on journalistic integrity. I’d want to find new ways to fund reporters and media outlets that don’t force them into generating click-bait stories. We need to return to fact-based journalism where stories are driven by truth rather than engagement metrics.
Many of our current challenges with misinformation and societal division can be traced back to the breakdown of traditional journalism models. If we could develop sustainable funding mechanisms that reward accurate, thoughtful reporting rather than sensationalism, we could make a massive positive impact on society.
How can our readers follow your work online?
You can follow my thoughts on AI security and technology on LinkedIn, where I regularly share insights about emerging security challenges and solutions. I also speak at major security conferences and contribute to various AI security initiatives through the Cloud Security Alliance.
https://www.linkedin.com/in/calebsima/
Thank you so much for joining us. This was very inspirational.
Guardians of AI: Caleb Sima Of CSA On How AI Leaders Are Keeping AI Safe, Ethical, Responsible, and… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.