Autonomous agentic AI promises efficiency and cost reduction, but confronts companies with major security, governance and trust challenges.
The rise of agentic artificial intelligence marks a major turning point in technological evolution. Capable of acting independently, this new generation of AI offers considerable potential in terms of efficiency and cost reduction. But it also raises crucial security, governance and trust for businesses. Their ability to master these issues will be decisive for the success of their digital transformation.
Between opportunity and vulnerability
We entered the era of agentic AI. Unlike traditional systems, these autonomous agents make real -time decisions, initiate actions independently and interact fluidly with APIs, cloud services and corporate data. Their potential goes far beyond automation, opening the way to operational autonomy which is revolutionizing areas such as logistics, customer relations, finance and cybersecurity.
However, this advance is accompanied by new risks. Agenic AI blurs the identity boundaries between humans, machines and digital agents. It widens attack surfaces by multiplying interconnections, and threatens data integrity if it were to be exploited or manipulated. Without a robust digital confidence base, companies are exposed to cyber attacks, data violations and critical failures.
The economic attraction linked to AI is however undeniable. Gartner estimates that on the horizon 2029, the use of IA agents could reduce operational costs associated with current customer service issues by 30 %. According to the 2025 Cybersecurity Innovations Survey, more than half of the companies surveyed (53 %) say they have already deployed personalized AI agents. Investors, for their part, encourage to accelerate adoption to generate tangible profits, especially in terms of efficiency and cost reduction.
But the reality on the ground remains contrasted. Many companies engage in experimentation without having measured and anticipated the infrastructure requirements necessary for large -scale deployment. The management of confidence, identity and life cycle becomes exponentially more complex when thousands of agents interact via cloud and API environments.
The PKI: an essential base
The Context Protocol (MCP) model, in the process of becoming a standard interface between AI, data and applications, illustrates well the current issues. Still a stammering, it is frequently based on implementations without advanced safety mechanisms essential for business environments, such as access control based on roles or automated identifiers. The persistent use of fixed keys or shared secrets is ultimately a major vulnerability.
In this context, certificate by certificates and the PKI (public Key Infrastructure) appear as an essential base. They offer proven responses to secure large -scale non -human identities. The Mutual TLS (MTLS) allows agents to authenticate each other before any data exchange; Ephemeral certificates reduce time the confidence granted to an agent; And the material keys strengthen the protection of on -board systems such as drones or vehicles. Cisco also recalls that identity -based attacks already represented 60 % of cybersecurity incidents in 2024, an alert signal at a time when the agentic AI is multiplying the exhibition points.
Agentics: big questions to anticipate
For agency AI to be able to evolve in a responsible manner, companies must align their security executives, their governance policies and their strategic priorities. Three main axes emerge.
1. Safety and scale
The agentic AI is already deployed in sensitive environments and requires a high degree of autonomy to keep its promises in terms of efficiency and cost reduction. The measurement of return on investment, the appointment of a risk manager and the capacity to inform the board of directors on the state of preparation constitute prerequisites. But as thousands of agents interact via API and cloud environments, the management of confidence, identity and life cycle is quickly complex, involving long -term infrastructure and maintenance costs that should be anticipated from the pilot phase.
2. Fast innovation and proven safety
The speed of innovation exposes companies to the risk of neglecting security. However, a compromise agent can generate quick and difficult to detect damage. Businesses must integrate from the design of robust cryptographic identity, visibility and governance mechanisms, while providing response plans for major incidents. Truck standards such as PKI, Mutual TLS or certificate authentication are essential as the pillars of resilience. Conversely, practices such as the use of static secrets are a fragility in the long term.
3. Governance, ethics and responsibility
The growing integration of AI agents into operational processes raises legal, ethical and reputational issues. The implementation of dedicated governance committees, supervision mechanisms and access revocation devices is essential. Without a framework equivalent to that applied to employees (HR, performance, regulatory constraints), agents are likely to act without effective control. The responsibility of companies with regard to regulators and the public will depend on their ability to demonstrate that they have verifiable systems and revocable confidence.
The agentic AI is therefore not only a technological innovation. It obliges companies to rethink their foundations in terms of security, governance and responsibility. Adopting proven executives, investing in an evolutionary identity infrastructure and aligning policies on this new reality are no longer options, but conditions of success.
The future belongs to those who will be able to establish confidence in a world where human and digital agents are already collaborating on a daily basis.
