AI agents are evolving rapidly, and with this evolution comes a significant risk: their memory systems need the same security measures as databases. Allie Miller notes that large language models (LLMs) are changing so quickly that keeping up is challenging. The real difficulty lies in effectively utilizing memory for AI agents. Memory acts like a hard drive for these systems, enabling them to function meaningfully. Without memory, an AI agent is just an advanced random number generator. However, integrating memory also opens new vulnerabilities. Organizations often treat agent memory like a temporary storage solution, but it should be managed as a high-risk database. The distinction between LLM memory and agent memory is critical. LLM memory is fleeting, while agent memory is persistent and can influence future decisions based on accumulated knowledge. Once an agent can modify its memory, every interaction potentially alters its operational state. If this memory is flawed or compromised, the agent's decisions will be skewed. Three primary threats arise from this: memory poisoning, tool misuse, and privilege creep. Attackers can manipulate an agent’s memory, misuse its abilities, or access sensitive information it shouldn't retain. The article argues that these threats are fundamentally data governance issues, akin to challenges enterprises have faced for years. As businesses transition from rapid deployment to ensuring governed data, it becomes essential to manage agent memory properly. Many frameworks create shadow databases with inadequate oversight, leading to security concerns. Instead of allowing agents to operate independently, their memory should be integrated into the existing data governance framework that safeguards sensitive information. Cloud providers are beginning to recognize this need, with solutions like Amazon's Bedrock AgentCore, which introduces structured memory management. However, many developers still opt for isolated storage solutions, leading to a lack of control and oversight. To mitigate risks, the article recommends establishing a clear schema for memory, treating every memory write as untrusted input, and implementing robust validation measures. These steps can help ensure that as AI agents evolve, their memory systems remain secure and reliable.