Artificial intelligence must remain in the service of humans

Artificial intelligence is advancing at a remarkable pace. It can analyze considerable volumes of information, write software, assist researchers, automate complex tasks, contribute to scientific discovery, and progressively become a genuine operational capability within organizations. With the arrival of AI agents, a new milestone has been reached, since it can now use tools, interact with systems, and carry out certain tasks with an increasing degree of autonomy.

These advances open up extraordinary possibilities. They also increase our responsibility: the more capabilities we give artificial intelligence, the more our own abilities to understand how it works, govern its use, secure its interactions, and intervene when an unexpected result appears must progress along with it.

This is precisely one of the concerns of the Center for AI Safety (CAIS), a non-profit research organization based in San Francisco that works on the risks associated with advanced artificial intelligence systems. Its approach rests on a fundamental idea: the considerable potential of AI can benefit society if its development and use are accompanied by sufficiently serious attention to safety.

Quantum Beyond shares this conviction. We believe deeply in the potential of artificial intelligence and in its ability to increase what organizations and their teams can accomplish. This ambition comes with a principle that must remain at the center of the transformation: technology must remain in the service and for the benefit of humans.

The Center for AI Safety occupies a particular position in the artificial intelligence ecosystem. Its role is not to commercialize a model or to promote the adoption of a particular platform. The organization works on the conditions that allow AI systems to progress while reducing the risks that may accompany increasingly significant capabilities.

Its work addresses AI safety from several angles. Technical research seeks in particular to better understand certain dangerous or undesirable behaviors, while other work draws on safety engineering, complex systems, international relations, and even philosophy. CAIS also contributes to the development of the field by supporting training, research, and access to a range of resources.

This multidisciplinary approach is particularly relevant because the challenges associated with artificial intelligence cannot be solved solely through more computing power or better models. They also touch on cybersecurity, organizations, human behavior, accountability, governance, and the mechanisms we choose to build around these technologies.

CAIS is known in particular for having helped bring to light certain extreme risk scenarios associated with the future development of AI. Its statement on artificial intelligence risk, supported by hundreds of researchers, technology leaders, and public figures, proposes that the extinction risk associated with AI be treated as a global priority comparable to other major societal risks. The signatories include several leading figures in the field, among them Geoffrey Hinton, Yoshua Bengio, Demis Hassabis, Sam Altman, and Dario Amodei.

The likelihood of such extreme scenarios naturally remains a matter of debate. That discussion is necessary, and it should not prevent organizations from drawing a far more immediate lesson from the reflection: when the capability of a technology increases, our ability to understand its behavior, limit its authority, and regain control when a situation demands it must progress at the same pace.

This question is already becoming very concrete in companies. An organization does not need to face an existential scenario to suffer the consequences of a misuse of AI. An agent with excessive permissions can cause an incident. Confidential information sent to an inadequate environment can produce a data leak. An erroneous recommendation used without sufficient validation can lead to a poor decision, while a poorly designed automation can reproduce an error at a speed and scale that a human could hardly achieve.

Artificial intelligence increases our capacity to act. That increase can simultaneously amplify the value created and the consequences of an error. Security, governance, and supervisory capability must therefore progress along with the autonomy granted.

This logic explains why several disciplines that sometimes appear separate must now be considered together. AI governance, AI Onboarding, IAM, Zero Trust and Continuous Trust, data and knowledge governance, cybersecurity, organizational transformation, resilience, and digital sovereignty all contribute to a single capability: enabling the organization to use increasingly powerful systems while maintaining sufficient understanding and control over their actions.

Hypersecurity takes on a particular meaning here. AI agents are no longer merely applications to be protected. They become digital actors that use identities, access knowledge, call APIs, communicate with systems, potentially collaborate with other agents, and can take certain initiatives. Cybersecurity remains essential to protect each of these components, while Hypersecurity makes it possible to observe their interactions within a global and evolving architecture.

The objective is to design an environment capable of anticipating, withstanding, detecting, containing, recovering, and learning when something does not go as planned. The more AI capabilities increase, the more important this cross-cutting perspective becomes, since risk can emerge from the combination of several elements that, individually, seemed perfectly reasonable.

Keeping artificial intelligence in the service of humans does not, however, mean placing a person behind every action of an automated system. Omnipresent human supervision would eliminate a large part of the benefits being sought and could even create an illusion of control when people end up mechanically approving the recommendations produced by the systems.

The real question is to determine where human judgment brings essential value. Some tasks can be fully automated when their level of risk and potential consequences are low. Others can be entrusted to AI with periodic supervision. Certain decisions warrant explicit human approval when they can produce significant financial, legal, operational, or human consequences.

This distribution must be designed intentionally. Autonomy is not a characteristic that the organization should grant indiscriminately to all of its agents. It is a capability that can be increased gradually according to role, level of risk, the quality of observed results, and the potential consequences of an error.

This is precisely one of the functions of AI Onboarding. Before an agent can fully take part in operations, the organization must define its role, the knowledge required for its work, its identity, its permissions, the systems it can access, the actions it can perform, and the situations in which a person must intervene. Autonomy then becomes something the organization governs rather than an implicit consequence of the technology it has purchased.

The same attention must be paid to the people who will work with these systems. A successful organizational transformation is not measured by the maximum number of human tasks that can be automated. It seeks instead to determine how technology can increase the collective capability of the organization and where human qualities remain particularly important: judgment, creativity, accountability, relationships, experience, intuition, empathy, and understanding of context.

Organizations will progressively have to learn to build hybrid teams in which people and agents hold complementary roles. Employees will need to understand the capabilities of the agents they work with, as well as their limits. Managers will need to learn to distribute responsibilities among different forms of intelligence, to determine what can be delegated, and to measure the value actually produced.

This transformation will therefore be technological and deeply organizational. It will modify certain processes, cause responsibilities to evolve, and could shift part of human work toward analysis, validation, judgment, creation, and exception handling. The value of AI will largely depend on how well this new distribution of work is designed.

Another dimension deserves the same attention: the knowledge we transmit to artificial intelligence systems. To be genuinely useful, agents must progressively understand the organization's products, processes, policies, customers, business rules, and working methods. This knowledge allows them to become far more relevant in their interventions.

It also constitutes a considerable organizational asset. Part of this knowledge represents several decades of experience accumulated by employees, executives, and specialists. It is part of the company's intellectual capital and deserves governance commensurate with its value.

The Qb Knowledge Standard – AI Readiness & Knowledge Governance addresses precisely this challenge. Before making an organization intelligible to machines, one must understand what knowledge exists, which of it is authoritative, who can access it, how it is maintained, and in which environments it can be used. Artificial intelligence can then amplify human knowledge while respecting and protecting what people have helped build.

This governance of knowledge connects directly with the governance of identities. When an artificial intelligence system accesses data, applications, and infrastructure, it becomes an active component of the information system. As soon as it begins to act, its identity, its permissions, and its behavior become operational cybersecurity concerns.

The principles of least privilege, Zero Trust, segmentation, distinct identity, traceability, and revocation then take on their full importance. An agent should have the access required for its role, its actions should be distinguishable from those of people, and its permissions should evolve with its responsibilities. When those responsibilities disappear, its access must be able to disappear with them.

This architecture is not intended to prevent AI from acting. It seeks to allow it to act within an environment designed to contain the consequences when unexpected behavior appears. This distinction is essential, since a mature architecture does not require believing that no error will ever occur. It considers instead the possibility of an error and builds the mechanisms needed to keep it manageable.

This approach also makes it possible to move beyond a sometimes artificial opposition between innovation and security. An organization that experiments rapidly with artificial intelligence without understanding its data, its permissions, its responsibilities, or its dependencies may obtain impressive results in the short term. It risks, however, having to slow down abruptly when an incident, a regulatory requirement, or an architecture that has become difficult to govern forces a rebuild.

Sufficiently solid foundations can, on the contrary, make acceleration easier. When the organization knows its data, governs its agents, controls their identities, and has mechanisms allowing it to adjust their autonomy, it can experiment more while knowing where the limits lie.

Security and governance thus become innovation capabilities. They give executives enough visibility to decide how far to go, teams enough rules to experiment, and agents enough autonomy to create value within an environment whose limits are understood.

Quantum Beyond aims to apply this philosophy to the daily reality of organizations. Our objective is to enable clients to make greater use of artificial intelligence where it brings real value, to automate certain activities intelligently, to increase the capabilities of their teams, and to discover new ways of working. This progression must simultaneously make it possible to know what the systems are doing, why they are doing it, what information they are using, how far they can act, and how a person can regain control when a situation requires it.

Behind these technical considerations ultimately remains a far more fundamental question: why are we developing and deploying these technologies? Their ability to analyze, automate, and act is a means. The number of automated tasks, the performance of a model, or an agent's level of autonomy should never become ends in themselves.

Artificial intelligence becomes genuinely interesting when it helps solve complex problems, accelerate research, improve services, increase access to knowledge, support workers, reduce certain repetitive tasks, and enable organizations to accomplish things that were previously difficult or impossible.

Technological progress thus takes on its full meaning when it increases our ability to improve human conditions. This orientation requires maintaining a clear intention behind innovation and continuing to evaluate technology according to the value it creates for people, organizations, and society.

The Center for AI Safety raises a question whose importance will grow along with the capabilities of artificial intelligence: how can we fully benefit from this technology while simultaneously developing our ability to understand and reduce the risks it can create?

This question goes far beyond the most extreme scenarios associated with the future of AI. It already applies to the daily decisions of organizations that deploy models, connect agents to their systems, transmit knowledge to them, and progressively grant them more autonomy. Every new capability should be accompanied by comparable reflection on governance, identities, permissions, knowledge, resilience, and the role humans must continue to play.

Quantum Beyond seeks to support this evolution alongside internal teams and their technology partners. The AI Governance Office, AI Onboarding, the Qb Knowledge Standard, IAM, Zero Trust and Continuous Trust, cyber resilience, and Hypersecurity are different dimensions of a single approach: increasing the organization's technological capabilities while simultaneously increasing its ability to understand, govern, and master them.

This approach makes it possible to keep humans in an intentional place. It is not a matter of asking them to mechanically approve every operation performed by a machine. It is a matter of preserving their authority over the decisions that matter, their judgment when the context demands it, their ability to intervene when limits are reached, and their responsibility in defining the objectives that technology must serve.

We believe deeply in the potential of artificial intelligence. That confidence is precisely what justifies building the conditions that allow it to be used with ambition, vigilance, and responsibility. The more its capabilities increase, the more our own capabilities for governance, security, and discernment will have to progress along with them.

At Quantum Beyond, technology is therefore not the destination. It is a means of increasing what humans can understand, create, protect, and accomplish. And if artificial intelligence does indeed become one of the most powerful technologies we have ever developed, this human purpose deserves to remain at the center of its evolution.