Enterprise AI agent adoption is creating a security challenge that traditional approaches weren’t designed to handle. When five development teams deploy hundreds agents across different frameworks, clouds, and architectures, security teams lose the ability to maintain consistent oversight.
The problem isn’t a lack of security capabilities, it’s that applying those capabilities requires understanding CrewAI versus LangChain internals, maintaining separate configurations for Kubernetes versus serverless, and coordinating with developers who prioritize velocity over security implementation. This doesn’t scale.
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.
The Heterogeneity Reality
Walk into any large enterprise deploying AI agents and you’ll find architectural chaos that makes centralized security management nearly impossible.
Team 1 built three agents with CrewAI, deployed them on AWS Kubernetes, and they consume Anthropic Claude through Bedrock. Team 2 has five LangChain agents running on Azure VMs, using a mixture of OpenAI GPT-4 and internally hosted Llama models. Team 3 deployed four agents through N8N workflows as serverless functions on GCP. Team 4 wrote custom Python agents running on legacy infrastructure, connecting to Together AI and Hugging Face. Team 5 built low-code agents in ServiceNow and Ray clusters.
Each team made rational choices based on their expertise and requirements. From a business perspective, this is velocity. From a security perspective, it’s fragmentation that traditional tools can’t span because they assume homogeneity that doesn’t exist.
Security teams are left trying to secure 25 agents with 25 different approaches, each requiring framework-specific integration and deployment-specific instrumentation, which means the security burden scales linearly with agent proliferation while security team capacity remains fixed.
What Policy as Code Means for Agent Security
Policy as code treats agent security configurations the same way platform engineering treats infrastructure—as machine-readable files that define what security looks like independently of application code.
Instead of asking developers to implement security controls differently across frameworks, we enable security teams to define policies that describe component topology, access boundaries, required security controls, identity requirements, and behavioral expectations. Developers build agents however they want—any framework, any LLM, any deployment model—as long as the deployed agent conforms to its security policy.
Our platform then adapts enforcement to the deployment environment automatically. For Kubernetes, we deploy as a DaemonSet with eBPF instrumentation. For serverless, we operate as an inline proxy. For specialized platforms like N8N or ServiceNow, we use native integrations where available and API interception where necessary.
Security teams write one policy per agent. Our platform handles deployment-specific implementation without requiring code changes from developers.
Outcomes This Enables
Unified Security Posture Across Heterogeneous Environments
Policy as code allows security teams to maintain consistent security standards regardless of how agents are built or where they’re deployed. The same policy definition works whether an agent runs in Kubernetes, serverless functions, or specialized platforms because our enforcement layer adapts to the infrastructure rather than requiring infrastructure to adapt to security tooling.
This means security teams don’t need to understand CrewAI versus LangChain internals, don’t need separate solutions for different cloud providers, and don’t need framework-specific SDK integrations. They define what security looks like once, and our platform enforces it everywhere.
Security Without Developer Friction
The policy as code approach removes security implementation from the developer critical path. Developers focus on building functional agents while security teams define and enforce policies through configuration rather than code review and SDK integration.
This matters because developer cooperation doesn’t scale across heterogeneous environments. When security requires developers to wrap every LLM call in SDK functions, implementations end up inconsistent—some developers comply religiously, others bypass controls for “performance reasons,” and some never implement them at all because of framework conflicts. Policy as code eliminates this variability by enforcing security at the platform level.
Context-Aware Security Beyond Access Control
Traditional access control lists define what agents can access but not what they should access in specific contexts. Policy as code enables us to encode semantic context that makes it possible to detect when agents use legitimate permissions inappropriately.
Consider an agent with permissions to read email and access cloud storage. From an RBAC perspective, both actions are authorized. But if the agent’s task is “summarize this meeting transcript,” email access and cloud storage scanning violate the semantic scope of that request even though they fall within the technical scope of the agent’s permissions.
Because policies define both permissions and purpose, our platform can detect this as semantic privilege escalation and flag it, require human approval, or block it entirely depending on enforcement settings.
Behavioral Baselines and Anomaly Detection
Policy definitions establish what normal behavior looks like for each specific agent—how many LLM calls per request, which data sources in what sequence, typical operation duration, and expected output patterns. Our platform learns these baselines over time and flags deviations that indicate potential security issues.
When an agent suddenly makes 10x more LLM calls than its baseline, accesses a data source it’s never touched, or exhibits timing patterns inconsistent with its policy definition, we can detect this as anomalous behavior. Because the policy defines the architecture, we can distinguish true anomalies from normal variance without generating excessive false positives that overwhelm security teams.
Flexible Enforcement Based on Risk
Policy as code supports both visibility and enforcement modes, giving security teams flexibility in how they apply controls based on agent risk profiles.
In visibility mode, our platform operates as a passive observer where traffic flows normally with negligible latency while we generate complete audit trails with security annotations. Deviations from policy are flagged but not blocked, making this ideal for development, testing, and lower-risk production agents.
In enforcement mode, we actively mediate agent communications where policy violations are blocked at runtime before actions execute. This introduces approximately 20ms latency per agent-to-tool interaction but prevents semantic privilege escalation and behavioral anomalies in real-time, making it essential for high-risk agents accessing sensitive data or taking irreversible actions.
The same policy definition supports both modes, which means security teams toggle enforcement based on agent risk profile and operational maturity rather than requiring architectural changes.
Incremental Adoption Without Disruption
Policy as code supports gradual rollout that allows security teams to establish control over existing agent deployments without disrupting operations.
Our platform scans infrastructure and identifies deployed agents through network traffic analysis, then generates initial policies automatically based on observed behavior. Security teams review and refine these auto-generated policies before deploying them in visibility mode where existing agents continue operating unchanged while security gains complete audit trails and behavioral baselines.
High-risk agents move to enforcement mode first while lower-risk agents remain in visibility mode, which allows enforcement policies to be tuned based on real-world observations and false positive rates to be minimized before broad rollout. Eventually, new agent deployments require approved policies and enforcement mode becomes the default for production agents.
Cross-Layer Security Correlation
Policy as code enables our platform to enforce security across network, transport, and application layers simultaneously while correlating signals across all three layers to detect attacks that single-layer tools miss.
At Layer 3 (Network), we monitor DNS to detect if components attempt to communicate with unexpected domains. At Layer 4 (Transport), we validate TLS to ensure all external communications use proper encryption and certificate rotation. At Layer 7 (Application), we inspect bearer tokens to verify user context propagation and detect unauthorized service credential usage.
This matters because agent attacks don’t respect layer boundaries—an indirect prompt injection might arrive through application-layer content but trigger network-layer data exfiltration, while service credential substitution happens at the application layer but enables privilege escalation visible only at the network layer.
Managing Complexity at Scale
The policy as code approach addresses a fundamental problem in enterprise agent security: how to maintain consistent security posture when development teams are building agents in radically different ways across radically different environments.
Traditional security approaches assume you can either standardize how agents are built or accept that security implementation will be inconsistent. Policy as code offers a third path where security teams define what security looks like through declarative configuration while our platform handles the complexity of enforcing those policies across heterogeneous infrastructure.
This is important because agent proliferation is accelerating while security team capacity remains fixed. Organizations need security approaches that scale independently of how many frameworks developers adopt, how many deployment patterns emerge, or how many new LLM providers enter the market.
We built Acuvity’s policy as code capability to provide that foundation.
To experience Acuvity’s AI security platform, book a demo: https://acuvity.ai/book-a-demo/
