Google Exec Backs Responsible AI, Puts Energy and Cybersecurity Front and Center

Google security leader urges responsible AI growth, with energy and cybersecurity at the center

Should the U.S. slow AI development? Royal Hansen, vice president of privacy, safety and security engineering at Google, says the priority is responsible build-out, not a pause that lets others sprint ahead.

His stance is grounded in practical upside: better science, stronger critical infrastructure, and improved defense. The message to leaders is clear - move fast with guardrails, especially in energy and cybersecurity.

Responsible speed beats a freeze

"There's a lot of upside to using AI well, whether it's in energy production or healthcare or science," Hansen said. The counterweight is discipline - safety, privacy, and security baked into how teams build and deploy models.

That balance matters most in security. As he put it, "we need to keep people safe [and] help people learn to use AI well at the same time."

Energy is a strategic focus - and a systems problem

Hansen highlighted the "Genesis Mission," a collaboration among technology companies, the Department of Energy and the Office of Science and Technology Policy. The initiative, signed by President Donald Trump last month, aims to accelerate AI use for scientific research.

Federal agencies bring unique assets - national labs, deep talent, and high-performance compute. Pair that with AI (and, as it matures, quantum) and you get a cycle: better science, better energy systems, and a stronger innovation edge for the U.S.

For context on the public side of this effort, see the U.S. Department of Energy and the Office of Science and Technology Policy.

Cybersecurity: AI as attacker and defender

Attackers are already using AI to scale social engineering, obfuscate malware, and probe systems faster than before. Defenders are responding with AI-driven detection, response, and security analytics that operate at enterprise and internet scale.

The takeaway: the side that operationalizes AI in production - with real telemetry, automated response, and continuous evaluation - wins more often and at lower cost.

What executives should do now

• Stand up AI security governance: Define model risk tiers, data classifications, evaluation gates, incident playbooks, and vendor controls.
• Invest in AI-for-security: Use models for code scanning, phishing detection, log enrichment, anomaly scoring, and faster root-cause analysis.
• Plan for compute and energy: Map projected model demand to data center capacity, electricity availability, cooling limits, and carbon targets.
• Plug into public-private efforts: Explore consortiums and lab collaborations tied to energy and scientific computing; align research goals where it makes sense.
• Treat data as a security product: Minimize sensitive data in training, enforce strong access controls, and monitor for leakage across the stack.
• Upskill your teams: Give engineers and security leaders hands-on training with modern AI tooling and evaluation methods. If you need a starting point, see AI courses by job role.

Bottom line

Slowing AI won't make risks disappear. Shipping responsibly - with security, energy, and research collaboration front and center - is how the U.S. stays competitive and safe.