Autonomous is an idea that loosely combines the concepts of basic automation, orchestration, and systems that can act on their own. The research paper ‘Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy’ puts forward that, “When vendors brand automated scanners as ‘autonomous AI,’ they’re not just overselling – they’re misrepresenting the fundamental nature of their systems. Organizations may reduce human oversight precisely when it’s most needed.” A lot of cybersecurity tools describe themselves as autonomous, but it should be noted that there is still a human element involved.
A study ‘Enhancing cybersecurity through autonomous knowledge graph construction by integrating heterogeneous data sources’ notes that “constructing an efficient knowledge graph poses challenges,” and that even systems built to operate autonomously must be refined using “logical rules and graph analytic algorithms” to handle inconsistencies.
SOAR platforms are a good example, as they speed up responses by automating predefined actions. These platforms still require people to adjust them as threats evolve. The gap in marketing and the reality as the use of AI driven attacks grows in volume.
Techniques like autonomous knowledge graphs from diverse data sources can help identify threats earlier, but analytical judgment is needed to deal with messy and conflicting data.
Autonomous security systems are systems that can detect threats, decide how to respond, and take action on their own. In practice, the term blends basic automation, orchestration, and limited forms of independent action. Many security tools describe themselves as autonomous, but there is still almost always a human involved somewhere in the loop.
Even advanced approaches must contend with uncertainty, as ‘Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability’ puts it, “Security-related data are often incomplete, inconsistent, and distributed across heterogeneous sources,” which limits how independently systems can operate without human refinement.
Traditional security operations sit at the opposite end of the spectrum. Analysts manually review alerts, investigate logs, and coordinate responses. As attack volumes increase and AI-driven threats become more common, those teams often struggle with alert fatigue and limited staffing, especially in healthcare environments.
Rule-based automation improved this model by handling repetitive tasks through predefined actions. Tools can block known malicious IP addresses or quarantine suspicious files, but only when the activity matches existing rules. When attackers change tactics or exploit something new, those systems cannot adapt on their own and must be updated by people. That limitation is what separates basic automation from anything truly autonomous.
Autonomous security systems are designed to watch environments continuously, rather than relying on periodic scans or manual checks. Instead of waiting for a human to trigger an investigation, these systems continuously monitor activity across networks. They collect signals as things happen, which makes them better suited for environments where traditional scan-based security can easily miss short-lived or subtle attacks.
As the aforementioned study ‘Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability’ emphasizes, “In contrast to the present in-vehicle security measures, this architecture introduces characteristics and properties that enact self-awareness through system-level accountability. It implements hierarchical layers that enable real-time monitoring, analysis, decision-making, and in-vehicle and remote site integration regarding security-related decisions and activities.”
Decision-making in autonomous systems is also more contextual than in traditional tools. Instead of reacting to one alert at a time, the system weighs multiple factors, such as how confident it is that the activity is malicious, and the potential impact of a response might be. Information from threat intelligence helps the system connect the dots and avoid unnecessary disruption.
A recent Dove Press narrative review on AI in healthcare states, “Real-life incidents show that generative AI poses unique cybersecurity risks in healthcare, including data leaks, algorithm manipulation, and deepfake misuse, highlighting the need to integrate targeted mitigation strategies into clinical risk management frameworks.”
An excerpt from the above mentioned research paper on cybersecurity AI notes, “Automation extends human capability through programmed rules, while autonomy requires the system to exhibit agency – to make choices based on understanding, not just pattern matching.”
Basic automated tools follow predefined actions and lack the behavioral analysis or feedback loops needed to respond proactively to threats. Even platforms that orchestrate workflows mainly automate human-directed playbooks. They still depend on operator oversight and manual approvals for critical decisions, despite marketing claims of full autonomy.
These systems support decision-making rather than replacing it, requiring continuous retraining and lacking the ability to act or improve independently. Tools that ignore governance, compliance, or policy cannot be considered truly autonomous.
The same study states, “Only by understanding where automation ends, and autonomy begins can we build systems that truly augment human capability while maintaining appropriate safeguards in cybersecurity.” No system is flawless; even sophisticated models are vulnerable to false positives from unusual but benign behavior.
Generative AI systems like Paubox consider the full context of each message, including headers, body, attachments, and sender behavior, to judge risk, flag suspicious messages, or isolate them before a person even sees them. A Springer study notes, “Humans must retain control of AI and autonomous systems.” This means keeping people involved even when machines act quickly under pressure.
They can also respond automatically in certain cases; for example, when a business email compromise attempt is detected, the system might send decoy replies to slow attackers while recording all activity for investigation. The system then learns from what works and what does not, adjusting its detection methods to handle new tactics or evasive threats.
In the study, participants were able to predict the outcome of human-operated tasks correctly 68% of the time, but only 58% of AI-led tasks. This is an example that machine behavior can be harder to anticipate.
A knowledge graph is a structured representation of relationships between entities, such as devices, users, and events, used to detect patterns and anomalies in security data.
SOAR stands for Security Orchestration, Automation, and Response, a platform that automates repetitive security tasks while coordinating human oversight.
Rule-based automation uses predefined instructions to respond to known threats but cannot adapt to new or unexpected attacks on its own.