Agentic AI is revolutionizing government efficiency by enhancing robotic process automation and citizen services. Unlike traditional AI, it autonomously acts without constant human oversight, making it essential for resource-constrained agencies. However, this independence raises security concerns, such as vulnerability to exploitation and unintended behaviors. Agentic AI’s higher stakes include risks like misrouting sensitive information, often due to misunderstood data. The Model Context Protocol (MCP) facilitates AI agent interactions with systems but lacks robust security measures, leading to visibility and identity challenges. Agencies must adopt advanced protective measures, including secure gateways for logging and monitoring, alongside infrastructure-level guardrails. Traditional methods are inadequate; instead, AI red teaming practices should simulate potential threats to enhance security. By focusing on visibility, guardrails, and rigorous testing, agencies can safely leverage agentic AI, ensuring that its benefits support government missions without compromising national security. Properly secured, agentic AI can unlock transformative opportunities for public service.
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