NOMINATION HIGHLIGHTS

Akto is the purpose-built agentic AI security solution that secures a new attack surface: prompt-to-tool execution, unsafe action chaining, MCP trust boundaries, excessive permissions, and real-time autonomous agent behavior.

Most AI security products were designed for LLM safety, they test models in isolation, flag unsafe text outputs, and report. Akto was designed for AI autonomy. It secures systems that reason, choose tools, access sensitive systems, and execute actions. That requires a fundamentally different architecture, and Akto delivers it through two purpose-built products: Akto ATLAS and Akto ARGUS.

ATLAS secures the employee AI layer: discovering shadow AI usage across browsers and endpoints, governing how employees interact with AI apps, LLMs, agents, and MCP servers, and enforcing real-time guardrails on every interaction before unsafe data or behavior exits the organization.

ARGUS secures the AI agent application layer: protecting internally built agentic systems, monitoring agent runtime behavior in production, intercepting MCP traffic as an inline proxy, and enforcing controls over what agents can access, invoke, and execute.

What makes Akto the standout solution is execution depth.
– Agentic AI Discovery: Automatically discovers AI agents, MCP servers, tools, and connected resources across cloud environments, employee endpoints, browsers, and internal infrastructure via 50+ connectors. AI Agent Context Graph builds contextual visibility across agents, tools, resources, permissions, prompts, and action paths to show how AI systems actually behave, and fail, in production.

– Automated AI Red Teaming: Continuously tests agents and LLM-connected workflows against real-world attacks, prompt injection, tool misuse, privilege escalation, data exfiltration, and unsafe multi-step behaviors, backed by 4,000+ prebuilt and customizable test cases.

– Agentic Security Posture Management: Identifies misconfigurations, over-permissioned tools, unsafe data access paths, exposed MCPs, and policy gaps across the AI environment.

– Runtime AI Guardrails and Enforcement: The MCP Proxy sits inline, enforcing before unsafe actions complete. Five guardrail layers: content and policy (prompt injection, context poisoning, intent violations), sensitive information (PII filtering with automatic anonymization, secrets detection), tool guardrails (tool misuse, malicious invocation, name-to-description mismatch), code execution controls, and custom LLM-based policies. Every guardrail is independently configurable by server, direction, and enforcement mode (block or log). The built-in Playground validates every guardrail against real attack scenarios before deployment.

– Dual-Layer Architecture (Employee endpoint + Homegrown apps): Delivers unified coverage across both employee-driven AI usage and homegrown or cloud-deployed AI systems, eliminating shadow AI and hidden agent sprawl.