Artificial Intelligence (AI) is revolutionizing cybersecurity across industries, bringing new capabilities and unprecedented potential for innovation. However, it also introduces significant risks, as evidenced by recent high-profile breaches in prominent AI firms. To navigate these challenges, companies must reimagine their approach to cybersecurity, adopting strategies that prioritize data integrity and local processing. One promising direction lies in offline AI models—sophisticated systems that operate independently of cloud networks. My interview with CEO Tyler Saltsman of EdgeRunner AI, a pioneer in this domain, offers insights into how these models can reshape security protocols and inform practices beyond the defense sector.
Going with Data Gravity: Localized AI for Enhanced Security
The primary advantage of offline AI lies in its ability to process data locally, eliminating the need to transfer sensitive information to the cloud. Traditional models, such as those from OpenAI and Anthropic, depend on extensive cloud-based resources and third-party hosting. While this architecture enables large-scale processing, it also exposes organizations to a range of security and compliance risks. By contrast, EdgeRunner AI’s domain-specific models deploy directly onto the organization’s private data, maintaining the integrity and security of intellectual property (IP) assets.
This principle aligns with the concept of “data gravity,” where data’s value and sensitivity increase as it accumulates, pulling applications and services closer. The best strategy, therefore, is to keep the data in place and bring the intelligence to it. Industries such as finance, healthcare, and energy, where data protection is paramount, can particularly benefit from this approach. By keeping data on-premises, organizations reduce their reliance on third-party cloud providers, decreasing vulnerability to breaches while meeting stringent compliance standards.
Lessons from Defense: Adapting Military-Grade AI to Commercial Use
The defense sector has long been at the forefront of AI innovation, with systems tailored for high-stakes environments. Military applications demand robust, agile AI that can operate in decentralized and often resource-constrained conditions. These characteristics make defense-grade AI particularly appealing to other industries facing stringent regulatory requirements or operating under high security demands.
One standout example from EdgeRunner AI is its AI BattleBuddy, named Athena, which operates entirely offline and is designed for military roles and personas. Athena’s ability to function on any device, even in air-gapped environments, makes it a formidable tool for mission-critical operations. By enabling AI to run autonomously without internet connectivity, Athena provides a model for secure, adaptable AI that industries like healthcare or finance could emulate to ensure data privacy and operational continuity.
Moving Beyond the Cloud: Mitigating AI Vulnerabilities
Recent cyber incidents, such as the OpenAI hack and the CrowdStrike breach, have underscored the risks associated with over-reliance on major cloud providers like Amazon, Microsoft, and Google. A single faulty update, like the one that crippled CrowdStrike’s systems for four days, can have widespread repercussions. Moreover, these providers’ cloud-based models are often opaque, making it difficult to identify potential security flaws or malicious injections.
The alternative? Open, transparent AI models that are fully owned and controlled by the deploying organization. According to EdgeRunner AI, its models are specifically designed to offer full visibility into their operations, enabling organizations to audit and verify AI behaviors comprehensively. By shifting away from “black-box” systems, companies can safeguard against vulnerabilities and ensure that their AI remains secure, effective, and aligned with their operational objectives.
Tailoring AI to Role-Specific Needs: The Key to Security and Effectiveness
AI’s true strength lies in its ability to adapt to specific roles and operational contexts. For organizations in regulated sectors or those with distinct security requirements, generic AI solutions often fall short. The complexity of these environments demands AI models that are not just accurate, but also context-aware and aligned with organizational needs.
EdgeRunner AI’s strategy involves developing role-specific AI models tailored to individual responsibilities, particularly in government and military contexts where duties are clearly defined. For instance, in aerospace, the firm’s culturally aware AI models support Space Force and Air Force personnel, enhancing their ability to fulfill mission objectives. This adaptability enables AI to provide targeted support, whether in real-time decision-making or by compressing unstructured data into actionable knowledge at the edge.
Other industries, from manufacturing to critical infrastructure, can leverage similar models to optimize their operations. For example, in finance, tailored AI can transform complex regulatory data into insights that drive compliance strategies, while in healthcare, it can support clinicians by contextualizing patient data in ways that enhance treatment outcomes without compromising privacy.
Building Resilience in a Dynamic Threat Landscape
As cyberattacks grow more sophisticated, organizations need AI systems that can anticipate and respond to evolving threats. A critical principle in developing resilient AI is ensuring that models remain both adaptable and secure over time. This involves building in capabilities for continuous learning and updating, enabling AI to integrate new information without sacrificing security.
EdgeRunner AI’s approach emphasizes personalization—turning generalized AI into tools that understand and support unique roles within an organization. This personalized approach ensures that the AI remains relevant and effective in dynamic environments. By aligning AI development with the organization’s core operational requirements, companies can create robust systems that withstand the complexities of a constantly shifting threat landscape.
Applying Military-Grade AI to Broader Industries: A Blueprint for Success
The potential of offline, domain-specific AI extends far beyond the defense sector. In finance, healthcare, and critical infrastructure, where data security and reliability are paramount, the deployment of on-premises AI could redefine operational standards. By operating locally, these models eliminate the need for sensitive data transfers, mitigating security risks and easing compliance burdens.
Moreover, as the technology evolves, EdgeRunner AI envisions a future where millions of specialized AI agents augment human tasks across various industries. These agents, trained for specific roles and operational contexts, could automate complex workflows, support decision-making, and ensure that critical infrastructure remains resilient against emerging threats.
The Path Forward: Balancing Innovation with Security
To harness the power of AI without compromising security, organizations must strike a delicate balance between innovation and risk management. A “crawl, walk, run” approach, starting with domain-specific models and advancing to more complex applications, can help companies scale AI technologies responsibly. For sectors that lack stringent regulations, establishing clear guidelines around transparency, data privacy, and the ability to audit AI decisions will be essential to ensuring safe and effective adoption.
The future of AI in cybersecurity and beyond lies in transparency, control, and adaptability. By integrating offline AI models that align with the principle of data gravity, organizations can protect their most valuable assets, remain compliant, and drive innovation. As industries outside of defense begin to adopt these technologies, they will not only enhance their security posture but also unlock new potentials for growth and operational excellence.
About the Author
Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Thought Leaders and Content Creators: Unlocking the Potential of Generative AI for Innovative and Effective Content Creation. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.
