Artificial general intelligence (AGI) refers to a hypothetical form of machine intelligence capable of understanding, learning, and performing any intellectual task that a human can.
AGI would completely redefine the SOC.
In theory, AGI can contextually process massive volumes of threat intelligence data, distinguish between false positives to filter out the noise, and dynamically apply new detection logic in real time as attackers change their tactics, without any pre-set rules, all with human-like reasoning.
And we’re not that far away from achieving this level of autonomy either.
Gartner predicts that “by 2028, multi-agent AI in threat detection and incident response will rise from 5% to 70% of AI implementations to primarily augment, not replace staff.
Sounds too good to be true, right? But there are caveats to consider. In this blog, we’ll explore the potential benefits of AGI, along with the key risks every SOC leader and CISO should evaluate before moving toward full autonomy.
The benefits of AGI for the SOC
Autonomous threat detection: AGI can essentially process massive volumes of log data with the ability to identify threats that analysts might have missed during routine log monitoring and collection.
Analysts are already overwhelmed with the volume of false positives they handle. A study conducted by IBM found that 63% of daily SOC alerts are low-priority false positives, with 49% of SOC team members only getting to half of the alerts that they’re supposed to review within a typical workday. Not only does this hinder incident response, but it also ends up costing the organization in delayed threat mitigation or focusing on the wrong priorities.
Autonomous threat detection would drastically cut the number of false positives a SOC analyst must sift through to find the small percentage of critical threats and prioritize them based on the potential business impact unique to that organization. This would tremendously benefit SOC teams that spend a good portion of their day chasing false positives with no return.
Predictive threat modeling: Existing SOC tools, such as UEBA and SIEMs, aren’t nearly as effective at recognizing threats. Research showed that enterprise SIEMs miss 76% of all MITRE ATT&CK techniques. Without the ability to recognize an attacker’s TTPs, security teams are left without the contextual insight or telemetry needed to prioritize and mitigate threats efficiently.
How about the efficiency of other SOC tools?
A study found that 47% of SOC practitioners do not trust their tools to work as intended, while 54% say the tools they use actually increase the SOC workload instead of reducing it.
Many AI SOC tools are also limited by pre-trained datasets.
AGI would be able to recognize complex attack patterns based on behavioral anomalies, indicators of compromise (IOCs), anomalous network traffic, and historical attack data to predict potential adversarial attack paths. AGI will possess advanced contextual threat understanding and human-level reasoning to make more informed decisions and recommend automated mitigation steps.
Human-level reasoning: LLMs can already solve advanced SOC challenges, such as correlating threat intelligence data from multiple attack sources, summarizing cases, and assisting with triage automation. Now, imagine if those AI agents took on the same reasoning and logic as human analysts.
AGI would be able to understand attacker intent and assess business impact, which is where most commercial AI SOC platforms currently fall short. It could process key findings and complex threat investigations with the proficiency of a tier 1 or tier 2 analyst at machine speed while continuously learning and dynamically adapting to evolving threat patterns.
It’s almost like playing a strategic game of proactive threat-hunting chess, where your SOC team is several steps ahead of the adversary’s next moves.
No human oversight. No analyst intervention. A fully autonomous SOC.
But is that really a good thing?
Risks and considerations of AGI for SOCs
Need for a human-in-the-loop: It is unlikely that AGI will replace a human SOC analyst. Gartner predicts that “by 2030, 75% of SOC teams will experience erosion in foundational security analysis skills due to overdependence on automation and AI.”
AGI, like its AI predecessors, will be vulnerable to new hallucinations and bias. It will require the oversight of an experienced analyst to validate findings and have the final say in critical decision-making.
Adversarial risks: LLMs are susceptible to a range of adversarial attacks, such as data poisoning, improper output handling, and prompt injection. AGI models will introduce new risks by enabling attackers to manipulate SOC decision-making processes and distort contextual reasoning.
An attacker could manipulate an AGI-powered SOC into prioritizing low-risk alerts, allowing critical threats to bypass detection logic and suppress escalation workflows. It could also rewrite incident timelines or delete valuable threat intelligence log data, further misleading analysts into applying incorrect remediation steps.
Data and privacy: AGI models may unintentionally leak sensitive data in their outputs if security guardrails aren’t in place. For example, an attacker might enter a malicious prompt requesting to know “if the ingested data logs contain any sensitive PII” and manipulate the AGI model to disclose usernames, email addresses, passwords, or credentials.
Gartner estimates that “by 2027, more than 40% of AI-related data breaches will be caused by the improper use of generative AI (GenAI) across borders.”
Without input validation and output filtering, attackers will have carte blanche access to a treasure trove of sensitive organizational data, which they can leverage to escalate privileges or leak onto dark web forums.
AI governance: Organizations must uphold ethical and legal obligations. Since AGI is uncharted territory, existing AI frameworks will become outdated, compliance regulations will evolve, and accountability standards will become more complex.
Human analysts may become overdependent on AGI’s reasoning and logic, overlooking bias in flawed outputs. This can create security risks, including bias-driven threat prioritization and incident reports containing misleading narratives.
AGI will be ingesting terabytes of logs, threat intelligence, user activity, and external signals at machine speed.
Who will challenge its logic when it doesn’t make sense?
How about bias control or output validation?
Just a few things to keep in mind with AGI.
Rethinking the SOC with Radiant’s Adaptive AI
The vision of a fully autonomous SOC powered by AGI is compelling, but in practice, it introduces serious risks that can’t be overlooked, from hallucinated outputs and adversarial manipulation to privacy leakage and analyst deskilling.
Radiant Security’s adaptive AI SOC platform bridges the best of human reasoning and machine intelligence without the downsides of ungoverned AGI. Radiant automatically triages 100% of alert types across all data sources and vendors, with full transparency into every decision it makes. Unlike AGI, which can become a black box, Radiant delivers explainable automation, allowing analysts to audit every action, verify threat paths, and trust the outcomes.
Where AGI may hallucinate or over-prioritize threats due to poisoned inputs or injected prompts, Radiant’s tightly scoped and rigorously validated detection logic prevents adversarial manipulation. It’s built to amplify human expertise, not replace it, preserving analyst oversight while automating the repetitive triage and enrichment workflows that stall traditional SOCs.
Radiant also eliminates data storage constraints that limit other AI platforms. It enables customers to retain and query all historical log data indefinitely at no additional cost, empowering threat hunters to draw from a richer context without needing AGI’s speculative reasoning.
Perhaps most importantly, Radiant was designed with AI guardrails from the ground up, including built-in output validation, secure prompt handling, and continuous learning under human supervision. In doing so, it delivers safe, scalable, and accountable AI for modern SOCs without waiting for AGI to mature or hoping it doesn’t go off the rails.
Radiant doesn’t just imagine the future of autonomous security. It delivers it today, responsibly.
Book a demo today and experience the future of SOC for yourself with Radiant.
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