When a critical intelligence update needs to reach Indo-Pacific coalition partners, every minute spent on manual classification review is a minute too long.
These classification bottlenecks aren’t just administrative hurdles — they’re operational barriers that can delay crucial military decision-making.
“In coalition environments, the traditional approach of manual classification review is not sustainable,” said Michael McKeon, IT Portfolio Manager for Mission Partner Environment – Engineering Services (MPE-ES) in the Indo-Pacific. “AI and machine learning solutions can automate these processes, ensuring that information is appropriately classified and distributed without the need for extensive manual review.”
Integrating new partners into coalition environments requires extensive data classification reviews across multiple stakeholder organizations, creating a significant increase in workload. Traditional manual review processes are not only resource-intensive but also introduce the risk of human error in classification decisions.
“AI and machine learning offer a transformative approach to this challenge,” McKeon said. “These technologies can analyze large amounts of data in real-time, identifying patterns and establishing consistent classification protocols that would be impossible to achieve manually.” By automating routine classification tasks, AI solutions free up security professionals to focus on more strategic activities while maintaining the highest standards of security.
Enhancing Zero Trust Architecture with AI-Driven Security Solutions
The SOSi MPE-ES team has conducted comprehensive research on technologies to implement within a Zero Trust Architecture (ZTA) framework. These efforts, currently in Initial Operational Capability (IOC) stages, are being developed in collaboration with software developers and security accreditation agencies to enhance the cybersecurity posture of coalition enclaves.
The integration of AI technologies into this ZTA framework brings several key benefits:
First, AI-driven detection software has demonstrated its effectiveness by successfully identifying simulated cyberattack activities during testing exercises. These solutions provide real-time threat hunting and intrusion detection capabilities, significantly reducing the time and effort required for manual security reviews.
Additionally, the MPE-ES Engineering team is leveraging AI-powered tools that allow users to perform advanced searches and create “pipelines” using natural language. This data engine enhances IT and security operations by providing improved data flexibility and visibility. The design strategy focuses on filtering and enriching data before it reaches data ingest engines, enabling more accurate and timely threat detection and response. When integrated with other distributed search engines, this approach significantly improves overall data management and analysis efficiency.
Building Trust in AI-Powered Coalition Environments
Implementing AI in coalition environments requires careful consideration of trust and security.
“If the users don’t trust that their data is being secure, then even that tactical solution will falter at the whim of those using it,” Col. Alton Johnson, assistant chief of staff, G-6 USARPAC said during a panel at the 2024 TechNet Indo-Pacific conference in Honolulu. Success depends on maintaining partner control over data while ensuring policy compliance and transparent audit trails.
The path forward involves a measured approach to implementation within a Zero Trust Architecture framework. McKeon suggested beginning with selective integration of AI-powered tools for automated access control and behavioral analysis into existing Identity, Credential, and Access Management (ICAM) solutions. This approach allows for strategic enhancement of threat detection capabilities while following a phased method to building trust.
“The key is establishing a baseline of normal operations and continuously monitoring for anomalies,” he said. “This proactive approach allows security teams to respond more quickly and effectively while maintaining the integrity of classified information.”
The future of coalition operations depends on finding scalable solutions to these classification challenges. AI and machine learning technologies offer promising capabilities, but their implementation must be guided by a commitment to security, trust, and operational effectiveness. By carefully balancing these priorities, we can enhance information sharing while maintaining the security standards that coalition operations demand.
