A new review argues that the next generation of cybersecurity could emerge from combining three technologies that are usually deployed separately: federated learning, artificial intelligence and zero-trust architecture. The proposed combination is designed to help organizations detect threats, protect sensitive data, continuously reassess access privileges and identify fileless malware—malicious software that can operate without leaving a conventional executable on a hard drive. Writing in the International Journal of Data Science and Analytics, Md. Mushfiqur Rahman and Sazzad Hossain of Samarkand State University describe how these systems could form a continuously adapting defense for cloud services, enterprise networks, internet-connected devices and critical infrastructure.
The appeal of the approach lies in the changing nature of cyberattacks. Traditional security systems often rely on known signatures, fixed rules or a perimeter separating a supposedly safe internal network from an untrusted outside world. That model becomes fragile when employees work remotely, applications run across multiple clouds and billions of devices exchange data. Attackers can also exploit stolen credentials, compromised endpoints or trusted connections that remain active long after a user’s behavior has changed. Zero-trust architecture, commonly summarized as “never trust, always verify,” responds by treating every access request as potentially risky. Rather than granting broad, permanent privileges, it evaluates identity, device condition, location, behavior and the sensitivity of the requested resource.
Artificial intelligence could make those evaluations faster and more dynamic. Machine-learning models can analyze network flows, login patterns, system calls, file access, process activity and other telemetry to identify behavior that differs from an established baseline. In a zero-trust environment, an anomaly might trigger stronger authentication, reduce a user’s permissions, isolate a device or block a connection. The technical challenge is that the most useful evidence is distributed across many organizations and devices. A hospital may observe one kind of attack, a bank another and an industrial facility a third. Sharing raw logs could expose personal information, trade secrets or security weaknesses, but keeping them isolated limits the data available for training robust detection models.
Federated learning is intended to solve part of that problem by moving the model rather than the data. In a typical federated-learning process, participating devices or organizations train a local copy of a machine-learning model using their own telemetry. They then send model updates—such as changes to neural-network weights—to a coordinating service, which aggregates the updates into a new global model. The raw data remains at its original location. The updated model can subsequently be distributed back to participants, allowing systems in different environments to learn from one another without creating a central warehouse of sensitive logs. This arrangement can support near-real-time threat intelligence while reducing some privacy risks associated with conventional centralized training.
Keeping data local, however, does not make federated learning automatically secure. Model updates can leak information, especially when an attacker analyzes repeated contributions or deliberately manipulates the training process. A compromised participant might send poisoned updates designed to make malware appear benign, while a malicious coordinator could attempt to reconstruct characteristics of local data. The review therefore places federated learning inside a broader security framework involving secure communication, participant authentication, anomaly checks on updates and, where appropriate, privacy-enhancing methods such as differential privacy or secure aggregation. Secure aggregation allows a coordinator to combine contributions without directly inspecting each individual update, although these protections can add computational overhead and may reduce model accuracy.
The proposed integration also extends to identity and access management, or IAM. IAM systems determine who can access which resources, under what conditions and for how long. AI-based IAM could use behavioral signals to supplement passwords, tokens and multifactor authentication. For example, an access request from a familiar account might still be treated as suspicious if it arrives from an unusual device, at an abnormal time, with an unfamiliar sequence of commands or alongside a sudden attempt to retrieve large quantities of data. Federated models could learn these patterns across distributed environments, while zero-trust controls could convert the resulting risk assessment into an immediate decision. In principle, this would allow access permissions to change continuously rather than remaining fixed until an administrator reviews them.
One of the most difficult targets described in the review is fileless malware. Unlike conventional malware, which may install a recognizable program on disk, fileless attacks can use legitimate tools already present on a system. Malicious instructions may be injected into memory, delivered through scripts, hidden in registry entries or executed through administrative utilities such as PowerShell. Because there may be little or no malicious file to scan, signature-based antivirus software can miss the attack. Detection instead requires behavioral analysis: monitoring parent-child process relationships, command sequences, memory activity, script execution, privilege changes, network connections and other events that, taken together, suggest abuse of a trusted tool.
Machine-learning systems can search this high-dimensional stream of endpoint and network telemetry for combinations of events associated with compromise. Supervised models learn from labeled examples of benign and malicious activity, whereas unsupervised or semi-supervised models identify unusual patterns when labeled attacks are scarce. Deep-learning systems can capture complex relationships across sequences of events, but they may be difficult to interpret and vulnerable to changes in the operating environment. A zero-trust response could limit the suspicious process, revoke a token, require fresh authentication or quarantine the endpoint while an incident-response team investigates. The review emphasizes that automated prevention must be carefully governed: an incorrect decision could interrupt a hospital system, halt an industrial process or lock out legitimate users.
The authors frame telemetry as the connective tissue linking detection and enforcement. Telemetry includes the continuously generated records of what devices, users, applications and services are doing. In a distributed architecture, these signals may be produced at endpoints, gateways, cloud workloads, containers and edge devices. AI can transform them into risk scores, while federated learning can help models improve across organizational boundaries without routinely exporting the underlying records. Zero-trust policy engines can then use the scores to make granular decisions about access. Such a system could be particularly valuable in industrial internet-of-things networks, healthcare platforms, financial services and other settings where systems are geographically distributed and the consequences of delayed detection are high.
The review is not a report of a completed deployment or a benchmark showing that the combined approach outperforms existing security products. No datasets were generated or analyzed, and the paper instead synthesizes prior research, compares the roles of AI, federated learning, zero trust and IAM, and identifies unresolved obstacles. These include communication costs between participants, inconsistent data quality, model drift as attacks evolve, false alarms, limited computing power on edge devices and the difficulty of explaining automated access decisions. Attackers may also target the learning process itself through poisoning, evasion or adversarial examples. Future systems will need stronger defenses against these attacks, standardized evaluation datasets, transparent policies, human oversight and tests in realistic operational environments. The central message is that privacy-preserving collaboration could make AI-driven zero trust more responsive, but the combination should be treated as a security research direction—not yet as a universal cure for cybercrime.
Subject of Research: Integration of federated learning and AI-based zero-trust architecture for cybersecurity, privacy, telemetry, identity and access management, and fileless-malware detection and prevention
Article Title: Federated learning and AI-based ZTA for security, privacy, telemetry, IAM and fileless malware detection and prevention framework: an in-depth review
Article References: Rahman, M.M., Hossain, S. “Federated learning and AI-based ZTA for security, privacy, telemetry, IAM and fileless malware detection and prevention framework: an in-depth review.” International Journal of Data Science and Analytics 22, 283 (2026). Original research page
Image Credits: AI Generated
DOI: 10.1007/s41060-026-01254-y
Keywords: federated learning, zero-trust architecture, artificial intelligence, identity and access management, fileless malware, cybersecurity, privacy-preserving machine learning, network telemetry
Tags: adaptive cybersecurity systemsAI-based threat responseAI-driven zero-trust architecturecontinuous access privilege reassessmentcybersecurity for enterprise networksdetection of fileless malwarefederated learning in cybersecuritynext-generation cybersecurity technologiesprotecting critical infrastructureprotection of sensitive data using AIsecuring internet-connected devicesthreat detection in cloud serviceszero-trust security models
