As our industry rushes to deploy AI agents, a critical new challenge is emerging for security teams. We're moving from a world of predictable software to one of autonomous, reasoning agents, and this shift requires us to evolve our security monitoring strategy.
For those of us who have spent our careers building and scaling security analytics platforms, this represents the next great frontier. The core question is no longer just 'what happened?', but 'why did the AI decide to act?'
A useful way to frame this challenge is 'AI Behavioral Assurance'—the need to ensure an agent's decisions are aligned with its purpose and policies. After working with SOC teams on the front lines, it's clear that the foundational platforms that can ingest and analyze data at scale are the key to solving this. This was perfectly captured in a recent conversation with a SOC analyst at a financial firm. He described agents as "black boxes with admin rights" and asked, "If one got nudged to act weird, what query would I even run to find it?"
His question gets to the heart of the opportunity.
The Problem: We're Monitoring Performance, Not Behavior
AI agents aren't just applications. They perceive, reason, and act autonomously. They call APIs, query databases, and interact with other systems. And they're proliferating in silos across the enterprise.
- Microsoft's Copilots live in the Microsoft ecosystem.
- Salesforce's Einstein agents live in their CRM.
- Your dev team's custom agents live... somewhere else.
This creates a fractured and dangerous reality. What I'm seeing is a consistent blind spot because our existing tools are often focused on operational questions.
Your APM tool asks: "Is the agent slow? Is it using too much memory?"
Your security analytics platform asks: "Did the agent's API call succeed or fail? Did it generate a log event?"
These are important, but they don't address the new, nuanced security questions:
- Why did the agent decide to access the customer billing database at 2 AM?
- Is its reasoning for that action consistent with its behaviour last week?
- Was its final response the result of a prompt injection or its actual logic?
- Is the agent subtly leaking PII in a way that doesn't trigger a standard DLP rule?
A traditional log entry showing API_CALL_SUCCESS provides the 'what,' but to detect these new threats, we need to enrich it with the 'why.' This requires a new analytical approach on top of the log data we're already collecting.
The Technical Opportunity: Evolving Our Analytics
To achieve AI Behavioral Assurance, we need to build new capabilities on top of our existing data platforms. I see three critical areas of focus.
First, "Behavioral DNA" Fingerprinting.
Every agent develops a unique operational baseline. What tools does it use? What is its typical tone and response length? By analyzing its activity data over time, we can automatically profile this "DNA" and then flag statistical deviations in real-time. An agent suddenly using a tool it hasn't touched in six months is a massive red flag that requires a deeper analytical approach to find.
Second, AI-Specific Threat Detection Logic.
An IDS isn't looking for prompt injections. A malware scanner can't detect a data poisoning attack that slowly changes an agent's behavior. We need to develop and deploy new detection rules and machine learning models within our analytics platforms that are specifically designed to find these new, subtle attack vectors.
Third, Decision Chain Reconstruction.
When an agent acts unexpectedly, an incident responder needs a flight data recorder. They need to visualize the entire forensic chain: Initial Prompt -> Agent's Internal Reasoning -> Tool(s) Selected -> API Call Made -> Final Response. This requires an application that can parse complex trace data and present it in an intuitive way, all powered by the raw data stored in our central platforms.
Why This is an Urgent Conversation
The clock is ticking, and the stakes are getting higher every day.
- Privileged Access: We're handing these agents the keys to the kingdom, granting them permissions to act on behalf of users and access our most sensitive data.
- The CISO's Mandate: Security leaders need a unified view of risk across all platforms. A siloed approach to AI security simply won't work.
- Auditors are Coming: Regulators and cyber insurance underwriters will soon demand an auditable trail of agent decisions, not just system logs. The EU AI Act is just the beginning.
My View: We Must Build a New Application Layer for Security Analytics
My view from this research is not that we need to replace our core security platforms. On the contrary, they are more critical than ever. We need to pioneer a new class of application built on top of our data platforms—an "AI Behavioral Security" application.
The SIEM or data lake becomes the indispensable data plane. The new application layer would perform the specialized tasks I mentioned earlier, leveraging our existing investment in data infrastructure while adding the specialized intelligence required for this new era.
This approach transforms the challenge into a huge opportunity: to deliver richer, more meaningful security insights than ever before.
Let's Define This Frontier Together
Solving the challenge of AI Behavioral Assurance is a massive undertaking that will require collaboration between security practitioners, data platform experts, and AI researchers. The conversation is just getting started.
I'm curious—how is your organization approaching the visibility challenge for AI agents? What capabilities do you think are most critical for a security analytics platform to provide in this new landscape?
Let's discuss in the comments.
The thoughts and opinions expressed in this post are my own and do not represent the official policy or position of my employer. This content is provided for informational and discussion purposes only.
