Determined cyber adversaries will find a way into government systems. The same reality is now true of AI agents finding their way somewhere they were never meant to go. In both cases, the ability to recover quickly and effectively should become a mission priority for the U.S. government and vendors. Otherwise, an outage caused by a rogue AI agent could stretch for weeks or months. Weeks or months of downtime can paralyze a commuter rail network or collapse a regional power grid during a heat wave.
Guardrails Aren’t Enough to Keep AI Agents in Check
Agencies have spent years hardening against outsiders trying to get in. The latest security challenge is authorized AI software, the next generation insider threat moving faster than any human can supervise, convinced it is being helpful. To counter this risk, agencies should preserve immutable backup data, maintain visibility into exactly what an AI system changed or damaged, and restore trusted systems quickly when autonomous actions go wrong.
Four failure modes matter:
Someone tricks the AI agent. Prompt injection works because an agent cannot tell information from instructions. An attacker could exploit AI vulnerabilities by hiding instructions inside content the agent will eventually read: white text in an email, a comment in a document, a line buried in a web page. Nobody has to click anything. The agent finds the instructions while doing legitimate work and follows them.
The agent breaks things by itself. No attacker required. One company founder publicly said his AI agent deleted a production database while troubleshooting, even after he told it not to run anything destructive.
Nobody can tell what happened. AI agents keep poor records of their own behavior. When something goes wrong, the questions “Why did it do that?” and “Which step caused this?” have no answer. Even more concerning, a rogue AI system could obscure its own actions by altering or deleting activity records.
The sandbox may not hold. Containment is meant to be a backstop when other safeguards fail. However, research shows those barriers can be broken. In one case, a malicious document exploited Microsoft 365 Copilot, escaped its isolated environment and created a path for an attacker to interact with the assistant and access data using the victim’s permissions.
Real-time anomaly detection and automated incident response carry real value; however, detection only tells you the building is burning. AI-driven recovery tactics set themselves apart by reversing damage in seconds, restoring clean data sets and rolling systems back to secure backups.
Choose AI Resilience Strategies Over Legacy Defenses
Federal AI policy should prescribe a detailed AI framework for translating operational requirements into production environments. Agencies should close the implementation gap by pairing practical guidance, applied research, executive education and operational best practices to make AI resilience a measurable discipline with clear ownership and repeatable practices, not another compliance checkbox.
Leaders should design AI agent systems so that people retain final authority over critical actions, while applying additional scrutiny and approvals to sensitive operations. They should also deploy strategies to evaluate an agent’s intent before execution and allow it to proceed only when the proposed action is understood and determined to be safe.
But authority means nothing if no one can see what the system did. Every deployment should log the actions taken, what triggered them and the prompts or tools involved. If an agentic system can’t be audited or explained, it doesn’t belong in sensitive government operations.
Visibility also makes recovery possible. Agencies should be able to roll back a single file, database or workflow without restoring an entire system. They should then test capabilities regularly through resilience exercises.
Treat AI Assurance as an Operational Requirement, Not an Afterthought
The biggest risks regarding rogue AI agents show up as weeks or months of downtime that can paralyze a commuter rail network or collapse a regional power grid during a heat wave.
Here, the impact of disruption is felt before it is detected. The organizations that scale agents safely will be the ones that can answer a harder question: “When an AI agent makes a harmful decision, can we see it, can we govern it, and, if it executes, can we reverse that action without tearing down the environment around it?”
Reversibility, not observability, is becoming the benchmark for how much autonomy the U.S. government can responsibly govern AI agents.
The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of Rubrik. These views are for informational purposes only and do not constitute business or legal advice. Organizations should consult with legal and compliance professionals to ensure their cybersecurity strategies meet all applicable federal, state, and international requirements.
Travis Rosiek currently serves as public sector chief technology officer (CTO) at Rubrik, helping government agencies become more cyber and data resilient. Rosiek is an accomplished cybersecurity executive with more than 20 years in the industry. His experience spans driving innovation as a cybersecurity leader for global organizations and CISOs to corporate executives building products and services. He has built and grown cybersecurity companies and led large cybersecurity programs within the Department of Defense (DoD). As a cyber leader at the DoD, he was awarded the Annual Individual Award for Defending the DoD’s Networks.
Prior to Rubrik, Travis held several leadership roles, including chief technology and strategy officer at BluVector, CTO at Tychon, federal CTO at FireEye, a principal at Intel Security/McAfee, and leader at the Defense Information Systems Agency (DISA). He has served on the National Security Telecommunications Advisory Committee (NSTAC) as an ICIT fellow and on multiple advisory boards.
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