Today we’re joined by Dominic Jainy, an IT professional whose work at the intersection of artificial intelligence and blockchain is shedding new light on one of the most pressing challenges in modern software development: security. As enterprises rush to adopt AI, Dominic has been a leading voice in navigating the complex authorization and access control issues that arise when autonomous agents and LLMs are given the keys to sensitive systems.
In our conversation, we’ll explore the dramatic surge in security risks tied to AI-generated code and the deep-seated problem of overpermissioning. Dominic will walk us through a developer-first, declarative approach to security that promises to mend the long-standing friction between development speed and safety. We’ll also delve into why AI’s unpredictable nature shatters traditional security models and what a “gold standard” for runtime, context-aware authorization looks like. Finally, we’ll discuss the practical steps for containing a rogue AI agent and look toward the future of access control in an increasingly autonomous world.
Reports indicate a dramatic spike in privilege-escalation risks from AI-generated code. What specific types of vulnerabilities are most common, and how does the routine overpermissioning of agents and LLMs turn a simple coding flaw into a major security breach? Please share a detailed scenario.
It’s a startling figure, isn’t it? That 322% jump in privilege-escalation risks really captures the heart of the problem. The core issue isn’t that AI is intentionally malicious; it’s that we are habitually lazy with permissions. We grant agents and LLMs far more access than they need, and this practice turns minor, almost mundane coding flaws into catastrophic security events. Imagine an autonomous agent designed for inventory management. It’s supposed to read stock levels and maybe update a logistics database. But in the rush to deploy, a developer grants it broad permissions across the entire e-commerce platform, including customer data and payment systems. The agent then generates a piece of code with a subtle vulnerability. An attacker finds this flaw. Without overpermissioning, the breach would be contained to inventory data. But with it, that simple flaw becomes a master key. The attacker can now escalate their privileges through the agent, moving from inventory logs to accessing and exfiltrating sensitive customer financial information. The initial vulnerability was small, but the excessive permissions created the disaster.
Many companies knowingly ship vulnerable code to meet deadlines. How does a declarative, developer-first security approach fundamentally change this dynamic? Could you walk us through the practical steps of using this method to define and validate a new access control policy before any code is generated?
That’s a painful truth—the statistic that 80% of companies push out vulnerable code under pressure is something we have to confront. The traditional model treats security as a final, often adversarial, checkpoint, which is why it gets bypassed. A declarative, developer-first approach completely flips that script. It’s about making security a collaborative design partner from the very beginning. Instead of a developer building a feature and tossing it over the wall to security, both teams sit down together at the outset. They use a structured, declarative format to define the policy. It’s like drafting a blueprint. For a new feature, they might write a policy that says, “This agent can perform read-only actions on the user database, can only call the external billing API with justification X, and all its actions must be logged for audit.” This policy isn’t buried in code; it’s a single, testable artifact. The developer can then validate their code against this policy locally, running automated tests to ensure compliance before ever committing it. It transforms security from a subjective, one-off review into a predictable, automated part of the workflow, eliminating the friction and the temptation to cut corners.
AI’s probabilistic nature challenges security models that rely on predefined pathways. How does this unpredictability break traditional access controls? Describe how a runtime, context-aware policy would evaluate and respond to an unexpected API call from an autonomous agent, ensuring it remains compliant and safe.
This is precisely where old security models crumble. Traditional access control is built for a deterministic world; it assumes that if you provide input A, you will always get output B. It relies on static access lists and predefined rules. But AI is probabilistic. It generates novel behaviors and can trigger API calls or data flows that no engineer explicitly programmed. An agent might decide, based on a new pattern it observes, to access a dataset it has never touched before. A traditional firewall or access list would simply see a request for a forbidden path and block it, potentially breaking the application. A runtime, context-aware policy, however, operates differently. When that unexpected API call happens, the policy doesn’t just check a static list. It evaluates the full context in real time: Who is making the request? Is it the verified agent? What data is it trying to access? And, crucially, what is the justification? The policy logic, which is machine-readable and versioned, can then make an intelligent decision. It might permit the action because the context aligns with its broader goals, log it for human review, or grant temporary access. This approach turns security from a rigid, brittle fence into a living, adaptive control layer that can handle AI’s unpredictability safely.
Achieving automated least-privilege access for AI agents is a key goal. What are the essential components of a system that can continuously analyze an agent’s behavior and automatically recommend policy updates or temporary access grants? Provide some metrics for measuring its effectiveness.
Automated least privilege is the gold standard for running agents safely in production. The system needs three core components to make this work. First, you need comprehensive observability—every single tool call, every data access, every action the agent takes must be captured and logged. Without this raw data, you’re flying blind. Second, you need an analytics engine that can process this stream of activity to understand the agent’s actual behavior versus its granted permissions. It’s constantly asking, “What does this agent really need to do its job?” Third, you need a recommendation and enforcement mechanism. Based on its analysis, the system should be able to automatically generate policy suggestions, like, “This agent has read-only access to the entire user database but has only accessed the ‘user_preferences’ table for the last 30 days. Recommend reducing its permissions to just that table.” It could also handle temporary grants for one-off tasks. In terms of metrics, you’d measure the “permission gap”—the delta between granted and used permissions—and track its reduction over time. You’d also monitor the number of automated policy updates successfully applied and the frequency of alerts for anomalous access attempts.
Imagine an autonomous agent begins exhibiting unusual activity, such as accessing data outside its normal scope. What immediate, actionable steps should security and development teams take? Detail the process for using permission controls to quarantine the agent and investigate the anomaly without disrupting the entire system.
When an agent goes off-script, the response needs to be immediate, precise, and controlled. The first step is containment, not panic. A modern authorization system should provide a “single action” quarantine capability. The moment an alert fires for unusual activity—say, an agent that normally handles marketing analytics starts trying to access HR records—the security team can instantly quarantine it. This doesn’t mean shutting down the entire AI pipeline. Instead, you can surgically downgrade its permissions to read-only, effectively defanging it. Or you could revoke its ability to call specific sensitive tools while allowing it to continue less critical functions. Once the agent is in this safe, quarantined state, the investigation begins. Because every action has been logged, development and security teams can collaborate, tracing the agent’s behavior step-by-step to understand what triggered the anomaly. Was it a malicious prompt? A bug in the model? Or an unexpected but legitimate edge case? This allows for a calm, forensic analysis without pulling the plug on the entire system, ensuring business continuity while resolving the threat.
What is your forecast for AI’s impact on authorization and access control over the next five years?
Over the next five years, I believe AI won’t just be the thing we need to secure; it will become an essential part of the security solution itself. The sheer scale and speed of AI-driven applications will make manual, static authorization policies completely obsolete. Instead, we’re going to see the rise of self-tuning, adaptive access control systems. These systems will use machine learning to continuously model the behavior of agents and users, automatically tightening or loosening permissions in real time based on observed needs and risk profiles. Authorization will stop being a set of rigid rules and will become a dynamic, intelligent fabric that is woven into our applications. The goal will be to create systems that can grant a “just-in-time, just-enough” permission for a single transaction and then revoke it microseconds later. This will finally allow us to deliver on the promise of true least privilege at a scale and speed that is simply impossible for humans to manage, making our systems both more agile and fundamentally more secure.