In the rapidly evolving world of decentralized finance, one of the most significant and disruptive forces is the integration of artificial intelligence. We’re moving beyond theoretical discussions into a reality where AI agents already dominate on-chain activity, creating what some call a “machine economy.” This new era, dubbed DeFAI, promises unprecedented efficiency but also presents profound security challenges. To navigate this complex landscape, we’re joined by Kofi Ndaikate, a leading expert in blockchain infrastructure and security, who will shed light on how we can harness the power of AI while defending against its inherent risks. We will explore why blockchains have become the preferred environment for autonomous systems, the dangerous “speed gap” opening up between automated attacks and human defenses, and the innovative concept of an AI-native immune system designed to protect the future of finance.
We’re seeing AI agents generate the majority of activity on some networks. What specific features of blockchain make it a superior environment for these autonomous systems compared to the traditional internet, and what are the next steps to make this infrastructure even more agent-friendly?
It’s a fantastic question because it gets to the heart of why this shift is happening now. For an AI agent, the traditional internet feels like a series of walled gardens. Every platform has its own closed API, its own siloed data, and its own unique set of rules. Imagine being an autonomous system trying to operate there—it’s a constant, frustrating process of custom integrations and permission negotiations. Blockchain, on the other hand, is a natively open and standardized environment. It’s a single, composable playground where data, execution, and liquidity are all interoperable. An agent can look at the full state of the system, interact with any protocol using shared standards, and move capital seamlessly. There’s no need to ask for permission or build a new bridge for every single interaction. The final piece of this puzzle has been the rise of low-cost layer-2 networks. With transaction costs plummeting, the economic barrier is gone. Agents can now afford to make thousands of micro-decisions a day, rebalancing and optimizing at a frequency no human could ever match.
The rise of AI seems to create a “speed gap” in security, where automated attacks can outpace human defenses. Could you describe the core challenges this presents for current security models and walk us through a scenario of what can go wrong when this gap is exploited?
The “speed gap” is perhaps the single most critical challenge we face. Our legacy security models are built around human reaction times. We rely on smart contract audits, bug bounties, and monitoring teams. But AI operates on an entirely different timescale. It’s erasing the old skill gap where a hacker had to be a deep technical expert. Now, bad actors can leverage specialized models to probe for vulnerabilities relentlessly. Imagine this scenario: an offensive AI agent is deployed to target a DeFi protocol. It doesn’t sleep. It doesn’t get tired. It methodically tests thousands of obscure edge cases that human auditors, despite their best efforts, might have missed over years of review. Suddenly, it finds a novel, non-obvious attack path, like the ones we saw in the Balancer or Yearn incidents. Within milliseconds, it executes a complex series of transactions, drains the protocol, and begins laundering the funds. By the time a human team gets an alert, the attack is over. The damage is done. Responding with purely human processes in a world of machine-time attacks is like trying to catch a bullet with a net. It’s simply not a viable strategy anymore.
Complex exploits, which can take years for even human experts to find, are becoming a major concern. How does a system like Sequence Level Security proactively identify and block these novel attack vectors at machine speed, and what makes this approach different from post-exploit analysis?
This is precisely where we need a paradigm shift. Traditional security is reactive; it’s about damage control. We analyze what went wrong after the funds are gone. Sequence Level Security, or SLS, flips this model on its head by moving security directly into the transaction execution layer, making it proactive. Think of it less like a post-mortem and more like an active immune system for the blockchain. Before a transaction is ever finalized and included in a block, the network sequencer simulates its effects. It doesn’t just look at the code in isolation; it analyzes the transaction in the context of the entire sequence. It looks for patterns, assessing whether the proposed state changes resemble known exploit behaviors or other malicious anomalies. It’s pattern recognition at machine speed. If the system detects a transaction that, for example, mimics the complex logic of a reentrancy attack or attempts a malicious state change that looks like a known exploit, it can instantly isolate and block that transaction. It never gets finalized onchain. The key difference is prevention versus cure. We’re stopping the attack before it can do any harm, operating at the same speed as the automated threat itself.
The concept of an “AI immune system” for a blockchain is powerful. Can you explain the step-by-step process of how a transaction is evaluated for threats at the sequence level and what key metrics or patterns the system looks for to differentiate a malicious bot from a beneficial one?
Absolutely. Let’s trace a transaction’s journey. When a user submits a transaction, it doesn’t go directly into a block. It first enters a pre-consensus stage where the SLS-enabled sequencer picks it up. Step one is simulation: the system runs the transaction in a sandboxed environment to see its exact outcome and what state changes it would cause. Step two is contextual analysis. The system doesn’t just look at this single transaction; it looks at the sequence of transactions it’s part of. It analyzes the execution patterns. For instance, is this a simple swap, or is it part of a multi-step process involving flash loans, unusual contract calls, and rapid token transfers that are characteristic of economic exploits? The system is looking for red flags—behavioral fingerprints of known attack vectors. The key to differentiating a malicious bot from a beneficial one, like an arbitrage bot, lies in these patterns and outcomes. A beneficial arbitrage bot’s actions, while complex, result in a predictable and balanced state change that stabilizes markets. A malicious bot’s transaction, when simulated, will often result in a clear, unbalanced drain of funds from a protocol or a state change that violates the protocol’s intended logic. It’s this deep, contextual analysis at the sequencer level that allows the system to make an intelligent judgment call and block the threat before it ever touches the main chain.
As more beneficial AI agents are deployed for tasks like yield optimization, the risk of them being targeted by adversarial AI also grows. How does an embedded security model create a stable environment for these “good” agents to operate safely and scale effectively?
This is a crucial point for the future of DeFAI. Productive automation depends on a predictable and reliable environment. If you’re going to deploy a sophisticated AI agent to manage millions in capital, you need to be certain that the underlying infrastructure is not a chaotic free-for-all where it can be easily preyed upon. Without robust, embedded security, the onchain world becomes too hostile. Every beneficial agent would need its own complex, bespoke defense mechanisms, creating a huge overhead and limiting its ability to scale. An embedded security model like SLS changes the game. By building protection directly into the transaction lifecycle, it creates a foundational layer of safety for everyone. It means that a yield-optimizing agent doesn’t have to constantly worry about being front-run by a malicious bot or having its strategy exploited by an unforeseen vulnerability in a protocol it interacts with. The infrastructure itself proactively filters out a significant portion of the adversarial noise. This stability is what will give builders and institutions the confidence to deploy more sophisticated and beneficial agents, allowing the true potential of DeFAI—intelligent capital routing, efficient liquidity management, and frictionless finance—to flourish.
What is your forecast for the evolution of DeFAI over the next three to five years?
Looking ahead, I believe we are at the very beginning of a Cambrian explosion for DeFAI. Over the next three to five years, the distinction between “AI” and “crypto” will blur significantly. We’ll move from the current state, where AI is mostly used for trading and arbitrage, to a far more integrated reality. I foresee autonomous agents managing entire treasuries, dynamically allocating resources across dozens of protocols to maximize yield and minimize risk in real-time. We will see the rise of AI-driven financial products that are constantly adapting to market conditions, offering levels of optimization that are simply unimaginable today. However, this future is entirely contingent on solving the security problem. The infrastructure must evolve. The winning platforms will be those that provide intelligent, embedded defenses as a native feature. Blockchains will be judged not just on their speed or cost, but on their resilience. The onchain economy is becoming a machine economy, and the only viable path forward is to build infrastructure that is intelligent enough to protect itself. If we get that right, the efficiency and innovation unleashed will be transformative.
