Kynan Carver is managing director of cybersecurity and technology services for Maximus, where he is responsible for expanding the cybersecurity practice for the company’s Defense Department team.
Emerging technologies are redefining the boundaries of the digital landscape, and with them, the rules of cybersecurity.
From generative AI and quantum computing to the ever-expanding universe of emerging technology, innovation is accelerating faster than security frameworks can adapt. The result is a new era of opportunity shadowed by complexity: every connected system is both a potential breakthrough and a potential vulnerability.
To secure this future, agencies must move from static defenses to intelligent ones and create a situation where AI enables cybersecurity to think, learn, and respond at machine speed.
Securing Emerging Tech: Building for Agility and Assurance
As digital transformation reshapes the mission, the old ways of doing cybersecurity has become obsolete. Emerging technologies – especially AI-powered systems and data-driven platforms – extend across hybrid and cloud environments, multiplying access points and risk factors. Protecting these systems requires security architectures that are adaptive by design.
Secure modernization starts with embedding protection into every layer of technology, not layering it on afterward. When agencies integrate security engineering, compliance, and operations into a unified, proactive framework, they gain the agility to confidently innovate. The goal is not only to defend against threats, but to ensure that innovation itself is secure, scalable, and sustainable.
AI in Action: The Advantage of Data
AI is transforming the way cybersecurity teams operate. Where analysts once struggled to keep pace with alerts and manual investigations, AI now enables faster, more precise decision-making. Natural Language Processing (NLP) can sift through unstructured threat data, such as incident reports or vulnerability bulletins, extracting patterns and preparing it for deeper analysis. Machine Learning (ML) models then detect anomalies in network activity or user behavior that might indicate compromise.
Large Language Models (LLMs) bring an even more transformative capability. By interpreting and generating human language, they can summarize alerts, surface relevant procedures, and even recommend immediate response steps. This can reduce analyst fatigue and dramatically accelerate the time to containment. These AI tools are then refined through continuous learning, training them to adapt to new threats and improve autonomously through feedback loops.
This interconnected “AI cyber toolbox” creates a dynamic defense ecosystem: one capable of recognizing subtle deviations, correlating signals across data sources, and prioritizing the highest-risk events. It’s an important, and long overdue, shift from reactive response.
Toward an Intelligent, Resilient Future
AI alone won’t solve cybersecurity’s most complex challenges. However, it does fundamentally change the balance of speed, scale, and precision. The combination of human expertise and intelligent automation allows agencies to strengthen defenses and stay mission-focused. The key is deploying AI tools and understanding how they interact, to use context and continual learning to turn raw data into actionable defense.
As the threat landscape continues to evolve, so must our approach. Intelligent defense means designing systems that can adapt, learn, and anticipate, so that security evolves as fast as the technology it protects.
In this new era of cybersecurity, resilience isn’t a reaction. It’s a capability, built for what’s next.
October is National Cybersecurity Awareness Month. WashingtonExec is sharing OpEds from industry experts on critical cyber topics, and how GovCons and government can work together to secure critical missions.