In 2023, a defense contractor’s artificial intelligence (AI)-powered threat detection system was compromised when attackers subtly manipulated its training data, leading to critical surveillance gaps that went undetected for weeks. This incident underscores the assertion that AI is a “transformative technology” requiring equally transformative security measures. As defense agencies rapidly integrate AI across mission-critical operations, safeguarding these systems against sophisticated adversarial threats has become imperative.

The Growing Dependence on AI in Defense

The utilization of AI in defense has skyrocketed over the past few years, and AI applications are now being embedded in everything from reconnaissance to threat detection, logistical planning, and cyber defense. As dependence on AI grows, so does the need for data. Not only does this data need to be vetted and verified, but it also needs to traverse security boundaries, including classified systems, from disparate sources to the AI database. This reliance underscores the need for specialized security measures that go beyond traditional cybersecurity frameworks, focusing instead on the specific needs and vulnerabilities of AI.

The Evolving Threat Landscape for AI

AI systems deployed in defense face advanced threats beyond the reach of traditional cybersecurity measures:

• Data Poisoning: Attackers inject corrupted data into AI training sets, which can degrade performance and lead to critical misidentifications. For example, embedding altered images, adversaries can manipulate a target recognition system to misclassify friendly forces as enemy assets. Learn more about steganography-based data poisoning attacks here.
• Model Manipulation: Through adversarial inputs, attackers can exploit or disable AI decision-making. By breaking the logic or manipulating prompting rules, an attacker can potentially force it to act in unexpected or unwanted ways, or even gain unauthorized access to privileged or classified information.

Let’s break down what these threats could mean in two real-world scenarios:

Data Poisoning in Battlefield Identification:		Model Manipulation in Personnel Protection:
Imagine an adversary subtly modifying AI training data for a drone-based target recognition system. A well-crafted data poisoning attack could mislead the system to identify friendly aircraft as threats or vice versa, risking lives and mission success.		By leveraging model inversion, attackers could extract confidential details about mission-critical personnel from an AI’s learned data patterns, revealing their whereabouts or movements. This creates a risk not just for information leakage but for personnel safety.

How Do CDSs Help Secure AI Systems?

Cross domain solutions with secure data validation and filtration mechanisms, create a secure infrastructure for data transfer to and from AI systems. Any data that is sent into the training models or data sets is verified and filtered according to security policy to help ensure the AI system is both protected from direct manipulation and that the data used is what it is supposed to be. In a sense, the CDS acts as a sort of immune system for AI environments to protect against model manipulation, data poisoning, and other adversarial actions.

One of the keys to enabling stronger AI systems is the integration of data from numerous sources, including data from both classified and unclassified environments. CDSs enable secure data sharing across networks of varying classification levels, with validation and filtering protocols that guard against data manipulation at each security boundary. This fine-grained inspection of data types and sources helps to ensure that only clean, authorized data enters sensitive AI systems.

Deep Dive: CDSs in Action – Protecting Each Layer of the AI Lifecycle

Cross-Domain Solutions (CDSs) offer critical protection across every stage of the AI lifecycle—from training and deployment to real-time monitoring:

Implementation Framework

Achieving effective CDS integration for AI in defense requires a phased approach:

Best Practices for Defense Leaders: Securing AI with CDSs

To maximize the effectiveness of cross-domain solutions in AI, defense leaders should adhere to the following best practices:

• Regularly Update and Test Data Validation Protocols: Ensuring proper ongoing operation is vital to maintaining security, and regular system updates should be accompanied by testing and validation protocols.
• Enforce Strict Data Governance Policies: Implementing and regularly updating data governance policies around data ingestion, storage, and transfer informs proper CDS system functioning and security policy compliance.
• Train Personnel in CDS Protocols: Effective CDS implementation depends on proper training. Ensure operators understand how to monitor for system disruption and respond to alerts.

Given the rapidly evolving landscape of cyber threats, static security measures are insufficient for AI protection. Continuous monitoring, in tandem with CDSs, ensures that emerging threats can be identified and mitigated before they compromise mission-critical AI systems or the outputs said systems produce.

Taking Action

It is clear that AI systems are here to stay and their utilization in defense can provide numerous potential decision-making benefits, improving time-to-action, targeting accuracy, and reducing collateral damage. Owl Cyber Defense provides approved cross-domain solutions that are ideal for integration with AI applications, including certified audio, video, and structured data transfers for AI security and intelligence dissemination.

Ready to learn more about how Cross Domain Solutions can securely enable your AI-augmented systems?
Download our White Paper Enabling & Securing AI in Defense now!