For two years, enterprise AI meant chatbots and copilots. That era is ending. Organizations are now deploying AI agents that don’t just respond to prompts but take action autonomously—managing workflows, executing code, sending emails, all with your credentials and permissions. Traditional security can’t protect against an agent that has authorized access but drifts from your intent. Every action passes the permissions check. The credentials are valid. The behavior, however, may have nothing to do with what you asked the agent to do. Agent Integrity is the framework for addressing this problem, shifting the security question from “can this agent access this resource?” to “should this agent be accessing this resource right now, for this task?”
The Clawdbot Dumpster Fire: 72 Hours That Exposed Everything Wrong With AI Security
This past weekend, security researchers discovered over 1,000 exposed Clawdbot servers, demonstrated credential theft via prompt injection in under five minutes, and documented supply chain attacks reaching seven countries. Here’s what enterprises deploying AI agents need to know.
The “Double Agent” Problem: Why Your AI Agents Can’t Be Trusted by Default
When you deploy an AI agent with access to your enterprise systems, you’re not granting access to static software—you’re granting access to a reasoning system that decides, moment by moment, what actions to take. The agent’s loyalty to your intent isn’t architectural. It’s inferential. And nothing in the architecture guarantees it stays aligned with what you actually asked for.
The Trust Layer Context Graphs Need
Foundation Capital’s thesis on context graphs captures where enterprise AI is heading—systems that accumulate decision traces and turn precedent into searchable institutional memory. But a source of truth is only as reliable as the actors that interact with it. If agents reason on poisoned context, exceed the scope of user intent, or generate inaccurate traces, the entire precedent base becomes suspect. Agent integrity is the missing piece that makes context graphs trustworthy.
Policy as Code: Managing Agent Security Across Heterogeneous Deployments
Enterprise AI agent adoption is creating a security challenge that traditional approaches weren’t designed to handle. When five development teams deploy hundreds of agents across different frameworks, clouds, and architectures, security teams lose the ability to maintain consistent oversight. At Acuvity, we use policy as code to solve this problem by treating agent security policies as declarative, version-controlled configuration that operates independently of how developers build their agents.
Semantic Privilege Escalation: The Agent Security Threat Hiding in Plain Sight
Traditional access controls check whether an agent has permission to act. They don’t check whether the action makes sense for the task at hand. Semantic privilege escalation is the emerging AI security threat that exploits this blind spot—and most enterprises aren’t equipped to detect it.
Agentic Application Security for Enterprises
Gen AI adoption has doubled to 65% from 2023 to 2024 and 75% of generative AI users are looking to automate tasks at work as per recent studies by Salesforce and Amplifai. When deciding to build or buy, companies reveal a near-even split: 47%…