America’s civil infrastructure stands at a crossroads as artificial intelligence rapidly reshapes both risk and resilience across critical systems. While the growing sophistication of AI-enabled cyber threats has exposed new vulnerabilities, it has also created powerful tools that, if governed correctly, can help infrastructure owners strengthen defenses and recover more quickly when breaches occur.
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Recent spikes in cyberattacks have underscored just how exposed essential systems such as power grids, water treatment plants and natural gas pipelines remain. Over the past decade, hundreds of reported intrusions by cybercriminals and foreign actors have targeted U.S. utilities, threatening public safety and operational continuity. Through August 2024 alone, cyberattacks on U.S. utilities surged by nearly 70% year over year — a trend that has only intensified as AI tools have become more widely available.
The rapid commercialization of AI has fundamentally altered the cybersecurity landscape. What once required deep technical expertise can now be executed with minimal knowledge, dramatically lowering the barrier to entry for malicious actors. As the author notes, “hackers don’t need in-depth knowledge anymore — just a ChatGPT subscription and a Wi-Fi connection.” At the same time, infrastructure operators now have access to AI-powered systems that can identify threats faster and respond more intelligently than traditional tools ever allowed.
Even the most advanced security programs can no longer promise absolute protection in an era defined by AI-enabled attacks. Deepfake technology has already demonstrated its ability to bypass knowledge-based authentication systems used by banks and government agencies, with the global financial sector reporting a 393% increase in deepfake-enabled phishing attacks in a single year. For infrastructure operators still relying on older digital systems, the risk is even more pronounced.
This reality has forced a shift in cybersecurity strategy. Rather than attempting to prevent every possible intrusion, organizations must now focus on limiting damage and accelerating recovery. Properly installed firewalls, segmented networks and fail-safe systems allow operators to isolate compromised areas before an entire system is affected, ensuring continuity even during an active breach.
AI can also play a defensive role once attackers attempt to gain access. Systems trained on appropriate usage data can detect anomalies, such as unusual login behavior or unauthorized data changes, and automatically flag or isolate affected components. In an infrastructure environment increasingly reliant on sensors and internet-connected devices, this ability to compartmentalize systems functions much like a valve on a leaking pipe — stopping escalation before it becomes catastrophic.
Strong AI governance must extend beyond technology alone. Improved internal policies, workforce training and clearly defined digital safeguards are critical to reducing organizational risk. Training employees on data hygiene, secure AI use, prompt engineering and recognizing AI-generated phishing attempts is increasingly essential as large language models become embedded in daily operations.
Frameworks such as the NIST AI Risk Management Framework, paired with regular audits, help organizations establish consistency, ensure compliance and foster trust in AI systems. Without these guardrails, even well-intentioned AI use can create unintended exposure.
One of the most significant AI-related risks facing infrastructure operators is accidental data leakage. An analysis by the House Committee on Homeland Security estimated that 1 in 10 intrusions the U.S. faced in 2023 stemmed from improper credential access rather than sophisticated hacking. As workers increasingly rely on AI tools for routine tasks, the absence of clear usage policies raises the likelihood that sensitive information could be inadvertently shared with third-party platforms.
All it takes is a single instance of confidential data being pasted into a large language model for critical information to become exposed — often without employees understanding how that data may be stored or reused.
Looking ahead, organizations must prioritize technologies that reduce both the impact of attacks and the role of human error. Voice recognition, biometric authentication and deepfake detection tools will play a growing role in safeguarding infrastructure systems, but only if supported by continuous monitoring, rigorous testing and clear governance frameworks.
AI is not inherently a threat to civil infrastructure. When deployed responsibly, it offers unprecedented opportunities to enhance security, efficiency and resilience. However, understanding the data privacy risks and new vulnerabilities that accompany AI adoption is just as important as modernizing outdated systems.
As the author concludes, the path forward depends on embracing innovation while investing in people and proactive governance. With the right balance, America’s infrastructure can not only withstand today’s digital threats, but emerge stronger and more adaptable for the challenges ahead.
Originally reported by Lalitha Krishnamoorthy in Construction Dive.
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