Codenotary, leaders in assuring safe and secure use of AI, today announced AgentMon 3, the latest generation of its enterprise AI security platform, introducing adaptive runtime security policies. These continuously evolve as AI agents operate across an organization by learning from customer-specific workflows, observed behavioral patterns, and newly emerging threats. The platform secures more than 5 million AI agent interactions daily across enterprise customer environments and is now available through AWS Marketplace for streamlined enterprise deployment. This operational scale has provided Codenotary with extensive real-world insight into how autonomous AI agents behave in production, enabling the company to build adaptive security capabilities based on actual enterprise deployments rather than laboratory simulations.
As organizations rapidly deploy coding assistants, autonomous software engineering agents, business automation platforms, AI-powered customer support systems, and custom orchestration frameworks, traditional security models based on static allow-lists and manually maintained policies are proving increasingly inadequate. AgentMon 3 addresses this challenge by continuously learning from millions of real-world agent interactions while adapting to each customer's environment and incorporating intelligence from newly emerging threats. This enables organizations to secure AI at enterprise scale without creating an unsustainable operational burden.
AgentMon 3 Adaptive Security Model
AgentMon 3 redefines AI runtime security with an adaptive behavioral model built specifically for autonomous AI agents. Unlike conventional software, AI agents evolve constantly through new prompts, model upgrades, tool integrations, memory expansion, and workflow changes. Static security rules cannot keep pace with this dynamic landscape. Every action an AI agent performs continuously shapes a live behavioral baseline, allowing organizations to distinguish normal operations from risky or anomalous behavior in real time.
The platform replaces manual policy writing with dynamically generated, self-refining security policies based on how each organization actually uses AI. These living policies adapt automatically across teams, roles, agents, and workflows, creating precise security baselines unique to each environment. By continuously learning from legitimate behavior, software updates, operational changes, and emerging threats, AgentMon eliminates much of the manual rule-tuning required by traditional security tools and cuts policy maintenance by up to 80%.
AWS Marketplace Integration
AgentMon 3 is now available through Amazon Web Services Marketplace, significantly simplifying procurement and deployment for organizations already operating on AWS infrastructure. This integration positions the platform for immediate worldwide availability, providing enterprises with a scalable foundation for securing AI agents in production environments. The marketplace listing enables easier integration with existing cloud-native services and distributed multi-agent architectures, supporting enterprise AI environments spanning coding assistants, autonomous software engineering agents, internal AI systems, orchestration frameworks, and cloud-native services.
Runtime Monitoring and Compliance
AgentMon 3 closes a critical security gap in autonomous AI systems. Many AI tools depend on built-in permission prompts and allow-lists that developers often weaken or disable for speed. Because AgentMon monitors actual runtime behavior independently of those controls, it continues to detect high-risk actions even when native safeguards are bypassed, misconfigured, or turned off.
Every security decision is evaluated with deep context, including agent identity, permissions, historical patterns, data sensitivity, requested resources, prior human approvals, and live threat intelligence. This context-aware approach reduces false positives while improving detection of sophisticated AI-driven attacks. AgentMon also correlates intent with impact, using observed file access, network activity, credential use, process execution, and system connections rather than relying on agent self-reporting. This makes the platform resilient to prompt obfuscation, multilingual attacks, and evasion techniques that bypass text-only security filters.
All runtime decisions made by AgentMon 3 are cryptographically recorded in Codenotary’s immutable tamper-proof ledger, creating a verifiable audit trail for compliance, investigations, and forensic analysis. This ensures organizations maintain full visibility into AI agent behavior while meeting regulatory and security requirements.
Key Takeaways
- AgentMon 3 introduces adaptive runtime security policies that evolve continuously based on customer-specific AI agent behavior and emerging threats
- The platform currently secures more than 5 million AI agent interactions daily across enterprise customer environments
- Available immediately through AWS Marketplace with up to 80% reduction in manual policy maintenance requirements
TechInsyte's Take
AgentMon 3 addresses a genuine operational challenge in enterprise AI security by automating policy adaptation at scale. The platform's focus on runtime behavior monitoring rather than static rules aligns with the dynamic nature of autonomous AI systems. However, the actual effectiveness of adaptive policies in complex enterprise environments remains to be seen, and buyers should evaluate how the platform integrates with their existing security toolchains and compliance requirements.
As enterprises increasingly rely on autonomous AI agents, the need for adaptive security solutions like AgentMon 3 becomes critical. By leveraging runtime behavior monitoring and AWS Marketplace integration, Codenotary aims to provide scalable, compliant security that evolves with organizational needs. Organizations looking to secure their AI workflows can now access AgentMon 3 through AWS, streamlining deployment and integration with existing cloud infrastructure.
Source: Businesswire
