Artificial intelligence is quietly redrawing the front lines of national security, and the United States’ Defense Industrial Base (DIB) now sits in the crosshairs of a new style of conflict. What once looked like routine cyber incidents now appears as a persistent campaign that fuses digital intrusion, supply chain manipulation, and targeted information operations into a single pressure system aimed at the factories, laboratories, and logistics networks that keep American forces armed and ready.

When most Americans imagine national defense, they focus on the uniformed force and the platforms that dominate headlines. Behind that visible edge of power stands a sprawling, largely private ecosystem of designers, manufacturers, software firms, logistics providers, depots, and research institutions that convert national intent into fielded combat capability. Federal regulation describes this DIB as the combined government and private sector industrial complex that can research, develop, produce, and sustain weapon systems and their components at the scale required for military operations.

That chain is far more diverse than the marquee names on major defense contracts. It includes niche machine shops, regional logistics firms, university laboratories, software providers, and government facilities that share technical data and workflow information through a constant flow of digital exchanges. Design files, bills of material, maintenance records, and operational telemetry form the nervous system of this industrial organism. When the system is healthy, the nation can surge production and sustain readiness under stress. When it is strained, deterrence becomes much harder to demonstrate in a crisis.

This industrial nervous system is absorbing AI at high speed, often with limited guardrails. Across the DIB, machine learning supports predictive maintenance, defect detection, demand forecasting, scheduling, and supply chain visibility. Corporate leadership often has only a surface-level understanding of how thinking machines can erode corporate identity, trust, and reputation quietly, yet catastrophically.

For the strategist, this adaptation expands the terrain adversaries can probe. Industry surveys in the World Economic Forum’s Global Cybersecurity Outlook 2026 report show that executives see AI as the primary driver of change in cybersecurity and regard AI-related vulnerabilities as the fastest-growing category of cyber risk. Organizations recognize that their attack surface is expanding as AI tools move into sensitive workflows, yet governance practices and security controls often lag deployment.

Hybrid campaigns against the DIB rarely announce themselves through spectacular outages. Security officials describe a patient pattern that begins with access and aims for endurance rather than immediate disruption. Once inside a network or supplier ecosystem, adversary teams map production rhythms, supplier dependencies, identity systems, and critical decision points where even modest delays can ripple across schedules and inventories. AI has become a force multiplier

for this reconnaissance, mining engineering files, and communications at machine speed and surfacing patterns that would take human analysts far longer to identify.

The contest is unfolding below the threshold of open conflict. Strategic competitors apply pressure through cyber intrusion, intellectual property theft, data manipulation, counterfeit insertion, and narrative operations designed to erode confidence in the U.S. capacity to arm itself and its allies. Hybrid pressure often manifests as subtle slowdown, rising cost, and creeping doubt rather than overt sabotage.

The stakes for deterrence are significant. Modern deterrence depends not only on visible platforms and war plans but also on an adversary’s belief that the United States can sustain forces and generate combat power at speed. If competitors conclude that AI-enabled systems in the DIB are brittle or easily manipulated, the credibility of deterrence weakens. The 2026 National Defense Strategy emphasizes strengthening the industrial base as a pillar of national power, tying cyber resilience directly to the ability to deter and wage war.

Pentagon planners increasingly describe cyber activity against the DIB as a persistent campaign environment. Adversaries can pursue national security effects through intrusions into private networks and business processes that support critical programs.

The technical attack surface is broad because the DIB operates as a tightly coupled system. Engineering teams collaborate across companies, moving design data through shared tools and platforms. Components flow through multiple tiers of suppliers before the final assembly. A single compromised vendor can offer adversaries access to multiple programs. Hybrid campaigns exploit these seams.

The effect of these operations extends beyond physical outcomes. Manipulated or stolen data can become material for influence narratives that frame U.S. production as unreliable or compromised. Production delays caused by cyber incidents can be amplified to sow doubt among allies about American capacity.

Adversary teams also benefit from repetition. They have treated the DIB as a learning environment, refining tradecraft through continuous operations. Hybrid coercion in this context is deliberate and calibrated, remaining below the threshold that would trigger a national response while still generating uncertainty and doubt.

Emerging evidence suggests that agentic AI systems are beginning to automate portions of this intrusion lifecycle. Automated chains reduce the cost of probing many suppliers and narrow the window for defenders to respond. For smaller firms with limited security resources, the pressure can quickly become unmanageable.

Security experts argue that defending against such campaigns will require disciplined governance and architecture. Pre-deployment AI security assessment and continuous validation must become standard practice, covering model selection, data provenance, access control, logging, and periodic reevaluation.

Architectural choices will also play a critical role. Zero trust principles and micro segmentation can reduce the impact of intrusions by limiting movement across networks. For DIB operators, this means treating segmentation and strong identity controls as the default architecture.

The industrial context around these cyber efforts is already volatile. The aerospace and defense sector is managing supply chain disruptions and resource constraints while trying to scale production. This environment amplifies the effectiveness of cyber sabotage and influence operations because attacks land in systems that are already stressed.

The strategic logic behind adversary focus on the DIB is clear. By degrading confidence and throughput, competitors can undercut deterrence without open conflict. When intrusions are contained and production continues under pressure, the calculus of hybrid aggression shifts.

Hybrid warfare against the DIB aims to bend trust in the systems that generate combat power. If those trust systems are distorted through advanced technologies and coordinated information operations, the result is slower decisions, higher costs, delayed output, and a quieter form of coercion.

Mr. Greg Sharpe is a Fellow and the director of Communications and Marketing for the National Institute for Deterrence Studies and the Managing Design Editor for the Global Security Review. Greg has over 35 years of military, federal civilian, and defense contractor experience in the fields of database development, digital marketing & analytics, technology use case exploration and assessment, and as a USAF doctrine outreach and engagement analyst. The views of the author are his own.

Quiet Warfare: Bending Data and Perceptions in the Defense Industrial Base was originally published on Global Security Review.

This digital divide is not just about access to technology but also about the ability to harness AI’s potential for economic, social, and global political empowerment. The implications of the AI divide are leading to a tech race that impact geopolitical power dynamics, exacerbating competition major powers like the US, China, France, and India. Depending on how the race ends, it could swing the balance of power in a negative direction.

AI Paris Summit

The recent AI Action Summit held in Paris emerged as a pivotal event that highlights both the opportunities and challenges associated with this evolving AI tech race. This summit serves as a beacon of innovation, encouraging France and India to collaborate more closely, while also exposing the complex interplay of international reactions, particularly from major powers like the United States, United Kingdom (UK), and China.

The summit focused on expanding AI’s boundaries while respecting environmental and ethical obligations. France’s President Emmanuel Macron reaffirmed his nation’s commitment to AI innovation, while maintaining high regulatory standards that draw parallels to Notre Dame Cathedral’s restoration. Such a strategy will likely accelerate AI ventures and foster innovation.

However, the regulation proposed was also a noose around the neck of the summit’s emphasis—deregulation. It is worth considering whether drastic deregulation will lead to genuine advances or the weakening of certain crucial safety nets as the world grapples with how to handle ever expanding data and the fragmentation that result from the intersection of geopolitical interests and the private ownership of data and capability. The US and China approach the issue differently than Europe, whose adoption of the AI Act (2023) will see the first comprehensive AI regulation in the world.

Many wondered how agentic AI will impact the balance of power. Whether AI will have a net positive or negative effect is uncertain. The rate at which agentic AI is developed and adopted in the three main economic blocs of China, Europe, and the United States will determine much.

India-France AI Alliance

India and France are forming a partnership to leverage AI’s full potential globally, sharing knowledge, resources, and best practices to create a robust framework for AI initiatives and global dialogue. During the summit, leaders from both countries addressed how their alliance could facilitate joint ventures, research collaborations, and talent exchange programs. Prime Minister Narendra Modi and President Macron foresaw great potential for future cooperation in 2019 when they endorsed the Indo-French Roadmap on Cybersecurity and Digital Technology. India and France, founding members of the Global Partnership on Artificial Intelligence, aim to develop a safe, open, secure, and ethical AI for human development and global commons.

Global Reactions

As the world watches the developments in Paris, reactions from other global powers, notably China and the United States, added another layer of complexity to the conversation around AI regulation and collaboration. In recent years, both states have invested heavily in AI research and development, seeing it as critical to their national security and economic competitiveness.

China’s approach to AI governance contrasts significantly with that of France and India. While the Chinese government prioritizes rapid innovation with minimal regulatory oversight, this led to concerns about privacy, surveillance, and ethical implications. The US, on the other hand, grapples with debates around data privacy, corporate responsibility, and the potential for AI misuse. As a result, the responses from these two states illustrate differing philosophies about how best to harness AI’s potential while safeguarding public interests.

The US and UK declined to sign a declaration on “inclusive and sustainable” artificial intelligence at the Paris summit, in a blow to hopes for a concerted approach to developing and regulating the technology. The document was backed by 60 other signatories on February 11, 2025, including France, China, India, Japan, Australia, and Canada.

The reactions from the relevant stakeholders highlight the urgent need for international cooperation and dialogue. As AI transcends borders, creating harmonized standards and frameworks could mitigate risks associated with its deployment.

Opportunities and Challenges Ahead

The summit concluded by emphasizing the need for proactive state engagement in AI regulation, prioritizing innovation while protecting individual rights and societal values. This, participants believed, would allow the world to fully capitalize on AI’s benefits.

As the world grapples with the realities of increasingly AI-driven technology, the voices of diverse stakeholders, including technologists, ethicists, policymakers, and civil society must be heard in shaping the future of AI. Under the flag of the AI Paris Summit, the India-France AI alliance is entering into a new and more unpredictable phase. By defying red tape and cultivating a spirit of cooperation, states are setting the pace for unlocking unprecedented opportunities while highlighting the inherent challenges of this transformative technology.

Huma Rehman is a project consultant, consultant, and defense and foreign affairs analyst. She can be reached at X @HumaRehman1.

AI Paris Summit and Emerging Paradox of AI Haves and Have-Nots was originally published on Global Security Review.

Agentic AI quickly became a buzzword in the technology industry, drawing the attention of innovators, corporations, and policymakers alike. By combining machine learning, natural language processing, and real-time analytics, agentic AI systems can interpret nuanced data and execute multi-step tasks previously thought to require human intervention. This capability unlocks immense potential across industries, from healthcare and finance to manufacturing and customer service.

However, the rise of this transformative technology is accompanied by profound challenges. The potential for significant job displacement is actual and imminent. Roles that involve repetitive or analytical tasks could be rendered obsolete, leaving swathes of the workforce vulnerable. This shift is sparking resistance from employees and unions concerned about job security and the ethical implications of AI in the workplace.

In the workplace, agentic AI is already making waves. Businesses leverage these systems to automate complex workflows, enhance productivity, and reduce operational costs. For instance, AI agents are being deployed to streamline supply chains, personalize customer interactions, and even assist in groundbreaking research, such as drug discovery and financial forecasting. As organizations increasingly adopt this technology, the workplace transforms, promising greater efficiency and innovation.

However, the rise of agentic AI is not without challenges. Concerns about job displacement, ethical considerations, and security risks have fueled resistance from employees, unions, and policymakers. Trust in AI decision-making and transparency in its operations remain critical issues that must be addressed for widespread acceptance. Moreover, questions about accountability and regulatory oversight are at the forefront of discussions as the line between human and machine decision-making becomes increasingly blurred.

As this trend continues to gain momentum, agentic AI promises to become a cornerstone of the future economy. Its ability to think, learn, and act autonomously offers unprecedented opportunities to reshape industries and redefine the relationship between humans and technology. With careful implementation and robust ethical guidelines, agentic AI could usher in a new era of collaboration between intelligent systems and human ingenuity, setting the stage for innovations yet to be imagined. 

Basics of Agentic AI

Agentic AI represents a significant advancement in artificial intelligence, characterized by its autonomy in decision-making and action execution without direct human intervention. Unlike traditional AI systems that rely on predefined rules, agentic AI adapts to dynamic environments, processes vast data, and formulates strategies to achieve specific objectives. This autonomy enables agentic AI to handle complex, multi-step problems across various industries.

Integrating agentic AI into the workplace transforms business operations by automating intricate tasks and enhancing efficiency. Companies like ServiceNow are embedding agentic AI into their enterprise software to act as control towers for AI-driven business transformations, facilitating seamless system cooperation. Additionally, organizations such as Johnson & Johnson and Moody’s are deploying AI agents for tasks ranging from drug discovery to financial analysis, demonstrating the versatility and impact of Agentic AI in various sectors.

Potential Benefits

Autonomy and adaptability are critical as agentic AI systems operate independently, making decisions and adapting to changing inputs to achieve specific goals without human oversight. Enhanced productivity is vital because agentic AI automates complex tasks, allowing employees to focus on strategic initiatives, thereby increasing overall productivity.

Agentic AI aids industry applications when it is utilized across various industries, including healthcare, manufacturing, and retail, to optimize processes and improve efficiency.

Emerging Concerns

 While promising enhanced productivity and operational efficiency, the integration of agentic AI in the workplace faces significant resistance from various groups due to various concerns. These objections stem from ethical, economic, and cultural implications. Worker advocacy organizations and unions express concern about the potential for large-scale job displacement.

Agentic AI, with its capacity to perform complex, multi-step tasks autonomously, threatens roles that rely on repetitive or analytical tasks, such as data analysis, customer service, and logistics coordination. These groups argue that businesses may prioritize short-term cost savings over employee welfare, exacerbating unemployment and wage stagnation.

Employees in affected industries, such as manufacturing, finance, and retail, fear job insecurity and skill redundancy. Their resistance stems from the perception that companies are deploying AI solutions without clear plans for retraining or upskilling displaced workers. Furthermore, employees often express discomfort working alongside autonomous systems due to a lack of trust and transparency in AI decision-making.

Civil rights groups and ethics committees raise alarm bells over the potential misuse of Agentic AI in surveillance, decision-making, and predictive analytics. Concerns center on the lack of accountability for autonomous decisions, biases in AI algorithms, and the possibility of violating individual privacy rights. These groups argue that deploying AI without clear ethical guidelines undermines trust and could harm marginalized communities disproportionately.

Many managers view agentic AI as threatening their authority and decision-making roles. With AI systems taking on responsibilities like resource allocation and strategic planning, resistance arises from concerns about diminished relevance and influence in organizational hierarchies.

C-Suite Leadership and Industry Best Practices

 Executives, particularly C-suite leadership, often resist large-scale AI adoption due to high implementation costs, cybersecurity risks, and the challenges of integrating AI into legacy systems. They also face pressure to ensure ethical compliance, which slows decision-making processes and creates resistance to adopting agentic AI systems.

As agentic AI reshapes the corporate landscape, C-suite leaders face a dual imperative: embracing this transformative technology, driving innovation, and remaining attractive to top talent while supporting existing employees. Forward-thinking executives recognize that successfully navigating this shift requires technical adoption and a strategic focus on building a resilient and inclusive workplace culture.

C-suite leaders emphasize reskilling and upskilling programs to keep talent coming. By investing in continuous learning initiatives, leaders can empower employees to thrive alongside agentic AI, positioning their companies as industry leaders prioritizing professional growth. Additionally, fostering partnerships with academic institutions and specialized training providers enables businesses to cultivate a pipeline of skilled professionals eager to work with cutting-edge technologies.

Equally important is addressing employee concerns about job security and ethical AI deployment. Transparent communication about how agentic AI is integrated and its benefits to the organization and workforce helps build trust.

C-suite leaders should also protect their organizations by establishing best practices for ethical AI use, including governance frameworks, data privacy safeguards, and inclusive decision-making processes. These measures mitigate risks and position companies as responsible innovators, enhancing their reputation among employees, customers, and investors.

 Ethics and Governance

 In the broader policy perspective, regulatory authorities resist rapid adoption due to insufficient legislation governing AI ethics, accountability, and security. Policymakers advocate for stricter oversight and clearer governance frameworks to mitigate autonomy and data security risks.

While this innovation has much to be praised, consumer resistance stems from a lack of understanding and trust in agentic AI systems. Concerns about transparency in AI-driven decisions—such as pricing models, recommendations, or dispute resolutions—lead to skepticism about businesses that fully rely on such systems.

 Tech Companies Eye Financial Windfall from Agentic AI

 Tech giants are racing to position themselves at the forefront of the agentic AI revolution, recognizing its immense potential to drive financial growth. Companies like Google, Microsoft, and Amazon invest heavily in research and development to create advanced AI agents capable of autonomous decision-making. These firms view agentic AI as a transformative technology that can enhance their existing platforms, open new revenue streams, and cement their dominance in the AI landscape. For instance, enterprise solutions powered by agentic AI are being marketed as tools to revolutionize industries by automating complex tasks, optimizing workflows, and delivering unprecedented efficiency.

Start-ups and established firms compete to attract venture capital funding, focusing on niche applications such as healthcare diagnostics, financial analytics, and personalized customer experiences. These targeted deployments promise significant cost savings for businesses, making them attractive investments. Additionally, tech companies are integrating agentic AI into cloud computing services, offering businesses scalable, AI-powered solutions that can be tailored to diverse needs.

In short, Agentic AI is coming. It will shape industry, government, and the military, and preparing for that future is critical.

Mr. Greg Sharpe is a Fellow and the director of Communications and Marketing for the National Institute for Deterrence Studies and the Managing Design Editor for the Global Security Review. He has 25+ years in marketing and communications with a focus on digital communications, organizational and institutional change, and analysis.  Greg has over 35 years of military, federal civilian, and defense contractor experience in the fields of database development, digital marketing & analytics, and organizational outreach and engagement, technology use case exploration and assessment.

Navigating the New Frontier: Agentic AI’s Promise and Challenges was originally published on Global Security Review.