The world is adapting to the concept of agentic AI: agents that can operate in your network with human instruction and direction, and cut the time needed to do menial tasks.
Within the SOC, a number of new tools and platforms are now vying for attention with a range of offerings for different sized users. We looked at five common criteria of the current offerings to determine the primary AI SOC analyst platform providers:
Autonomy (agentic AI capabilities)
Time-to-Value (out of the box)
Explainability (“black box” or transparent)
Integration (how well it plays with others)
Investigation Depth (alert summary vs cross-correlation)
These platforms are pulled from the ranks of incumbent, well-established vendors as well as startups and disruptors.
They all specialise in combining AI, ML, and automation to transcend manual processes and unilaterally improve security outcomes across the board. But to what extent and in what ways will vary.
AI SOCs are evolving the way we do cybersecurity; the investment a company makes now can have a game-changing influence on how their posture evolves in the coming few years.
1. Prophet Security
Best For: Companies that want to maximize the automation of alert triage and investigation while maintaining the flexibility of Human In the Loop and Human On the Loop (HITL/HOTL) models. Best-in-class integration depth, time-to-value, and explainability.
Rankings:
Autonomy: High. Complete automation of triage and investigation using a fully agentic AI SOC model. Adapts, reasons, plans, and queries without manual intervention.
Time-to-Value: Rated high in enterprise environments, Prophet’s offering is ready on day one to ingest alerts across sources and takes investigation times from hours to minutes.
Explainability: High. Operates with fully transparent reasoning and evidence for investigation plans, logic, and decisions. No “black box” method.
Integration: High. Completely vendor-agnostic orchestration with integration across SIEM, EDR, identity, cloud, phishing, and all major security signals.
Investigation Depth: High. Autonomous full-stack investigations with agentic AI agents: constructs investigation plans, gathers data across tools, correlates telemetries, tests and proves hypotheses, and emulates the reasoning of Tier 1 – Tier 3 SOC analysts.
2. Palo Alto Networks – Cortex XSIAM
Best For: Enterprises invested in the Palo Alto ecosystem looking for deep integration across existing tools – firewall, endpoint, cloud telemetry – and additional assistance from AI and automation. Unified analytics and platform consolidation at scale.
Rankings:
Autonomy: Average. An AI-driven SIEM/SOC that uses ML to accelerate response but ultimately leans on playbooks and automation rather than agentic AI.
Time-to-Value: Average. Strong automation and AI lead to faster detection and response, but the platform often requires data onboarding and configuration at the outset.
Explainability: Average. Explainability centered on contextual enrichment and automated analytics, rather than transparent evidence trails at each step.
Integration: Below average. Strong integration across the Palo Alto ecosystem but limited beyond that. Customers must adopt the whole platform to fully realise benefits, increasing vendor-lock in and reducing the ability to leverage best-of-breed capabilities.
Investigation Depth: Average. Investigations run primarily by prebuilt playbooks and analytics rather than autonomous reasoning. Alerts categorised by similarity and enriched with context.
3. Dropzone
Best For: Automating alert investigation workflows, structured case summaries, and “no-code, no-playbook” autonomous AI that integrates with SIEM/XDR environments to take on Tier1/Tier 2 tasks.
Rankings:
Autonomy: Above average. Autonomous AI runs investigations and performs pre-resolution actions like dismissing false positives and escalating events.
Time-to-Value: Average. Implementation can be difficult. Although deployment can occur quickly, users often report configuration, training, and tuning times of up to several months to see full value.
Explainability: Above average. Explains findings and decisions in plain English with added context from investigations and alerts.
Integration: Above average. Can integrate with many stacks (EDR, SIEM, identity, cloud) but focused on alert-centric workflows.
Investigation Depth: Average. Predefined analyst investigation techniques replicate SOC workflows but lean more on structured procedures than dynamic hypothesis testing and reasoning.
4. Darktrace NDR
Best For: Companies looking for a mainly network-based approach, with limited or no need to cover cloud-based environments.
Rankings:
Autonomy: Below average. Uses AI/ML for network-specific detection, not full-scope SOC investigations.
Time-to-Value: Below average. A significant time investment is required for the ML component to learn the behavior of the assets in the network.
Explainability: Average. Explains detection patterns for anomalous network behavior, but not full lifecycle reasoning across alerts.
Integration: Average. Works best when supporting SIEM/XDR but doesn’t provide unification across all enterprise sources.
Investigation Depth: Low. Investigations center on anomaly analysis and forensic reconstruction rather than investigative reasoning.
5. Google Cloud Chronicle
Best For: Enterprises wanting assistive, not autonomous, AI. Great for large companies needing to invest in speed and scale, with massive-scale log analytics and super-fast search; highly optimized for Google Cloud environments.
Rankings:
Autonomy: Below average. AI is often assistive in function, not agentic. The focus is on supporting human analysts with decision-making tasks.
Time-to-Value: Average. Produces scalable analytics rapidly, but configuration is required to realize full value.
Explainability: Average. Analytical context is provided for interpretation, but explainability typically ranks lower than in agentic platforms.
Integration: Above average. Strong integrations within the Google ecosystem; great for hyperscale cloud log analytics and deep insights across large telemetry datasets.
Investigation Depth: Average. Works well across large-scale telemetries but falls short in agentic AI; investigations driven mainly by analyst queries (expertise and time required) and predefined agent workflows.
6. Radiant
Best For: Teams looking for an AI-driven SOC co-pilot specialising in deep identity-centric integration. Radiant operates as a “single source of truth” for identity, unifying IAM data across LDAP, AD, and the cloud.
Rankings:
Autonomy: Above average. Strong AI-driven remediation with one-click action plans that can be launched manually or fully automated.
Time-to-Value: Above average. API-first, offering quick setup. Operational from day one.
Explainability: Average. Shows why alerts were categorized as benign or malicious and provides context to support conclusions; may not dive as deeply into the chain of logic on each step.
Integration: High. Matches leaders (Splunk, Prophet) in breadth, ingestion, and normalisation.
Investigation Depth: Average. AI-assisted SOC workflows, but analysts heavily involved in validating findings. Depth depends on workflow and integrations, not dynamic reasoning.
7. Simbian ai
Best For: An autonomous AI SOC that leans on Simbian’s Context Lake to guide decisions and incorporate existing policy documents. Best for orgs that want strong time-to-value, high explainability, and, while not as extensive as other ecosystems, solid integration.
Rankings:
Autonomy: Above average. Strong agentic AI capabilities but just a notch behind leaders (Prophet and Dropzone) in plan-and-execute reasoning without playbooks.
Time-to-Value: High. Good out-of-the-box capabilities with API onboarding and immediate autonomous operation.
Explainability: High. Strong transparent, step-by-step logic behind investigative decisions, though explainability depth may vary based on how well integrations surface context.
Integration: Above average. Support across SIEM, EDR, cloud, identity, and more; custom sources may require some bespoke integration.
Investigation Depth: Above average. Agentic SOC analysis with automatic alert investigation, cross-correlation, and verdicts with confidence scores. Strengths lie in classification and response, not full narrative, evidence-backed investigations.
8. 7ai
Best For: Teams looking for a multi-agent AI system that can perform autonomous AI SOC functions across a large scale and within distributed environments.
Rankings:
Autonomy: Average. Emerging muti-agent automation and improving autonomy, but not yet as mature as other offerings.
Time-to-Value: Average. Can deliver tangible security outcomes in days to weeks, rather than in quarters as traditional security timelines dictate. Deployment within days.
Explainability: Average. AI agents provide audit trails and investigation narratives populated into unified cases, but does not reach step-by-step reasoning explainability.
Integration: Average. Cross-domain integration: cloud, endpoint, identity, email, SIEM.
Investigation Depth: Above average. Autonomous AI agents cross-correlate, enrich and investigate. Approach focuses on coordinated agent workflows and automated analysis rather than fully agentic AI that mimics SOC reasoning.
9. Exaforce
Best For: Best for those that need SIEM capabilities in addition to AI SOC. “Exaforce” agents perform lifecycle-wide AI orchestration across detection, triage, and response.
Rankings:
Autonomy: Above average. High autonomy Exabot agents perform detection, triage, investigation, and response across the alert lifecycle.
Time-to-Value: Above average. Designed to accelerate operations and provide a rapid SOC lift, with deployment within days and broad coverage.
Explainability: Above average. Provides enriched context and detailed investigation workflows, but not transparent agentic AI decision-making reasoning.
Integration: Above average. “Connects the dots” across disparate tools (SIEM, SOAR, Jira, ServiceNow), but does not lean into analysing raw logs, asset data, or user behaviour on its own.
Investigation Depth: Average. AI-driven analytics power cross-telemetry correlation, but investigations rely on data correlations and analytics pipelines, not problem-solving AI agents.
10. Splunk AI SOC
Best For: Large enterprises looking for AI SOC capabilities embedded within a SIEM. Good for handling large amounts of data at scale and deep ecosystem integrations; however, autonomous investigation remains a weak point.
Rankings:
Autonomy: Below average. AI assistance in investigations; not autonomous AI. Relies on playbooks and SIEM analytics rather than agentic reasoning.
Time-to-Value: Below average. The cost of powerful analytics is often significant for onboarding, fine-tuning, and detection engineering.
Explainability: Average. AI Assistant displays steps to resolution and visibility into reasoning. However, audits depend largely on dashboards and analytics; explanations can require manual interpretation.
Integration: High. Excellent ingestion of a wide array of telemetry sources; broad data intake and integration power.
Investigation Depth: Average. In-depth investigations are largely analyst-directed with AI assistance across the SIEM environment. Investigations rely heavily on detection rules, SOC queries, and data exploration.
| AI Soc platform | Autonomy | Time-to-Value | Explainability | Integration | Investigation Depth |
| Prophet Security | 5 | 5 | 5 | 5 | 5 |
| Palo Alto – Cortex XSIAM | 3 | 3 | 3 | 2 | 3 |
| Dropzone | 4 | 4 | 4 | 4 | 3 |
| Darktrace NDR | 2 | 2 | 3 | 3 | 2 |
| Google Cloud Chronicle | 2 | 3 | 3 | 4 | 3 |
| Radiant | 4 | 4 | 3 | 5 | 3 |
| Simbian AI | 4 | 5 | 5 | 4 | 4 |
| 7ai | 3 | 3 | 3 | 3 | 4 |
| Exaforce | 4 | 4 | 4 | 4 | 3 |
| Splunk AI SOC | 2 | 2 | 3 | 5 | 3 |
Conclusion
It’s important to define which elements of an AI SOC you value most before making your choice. While all aggregate telemetries across tools, automate simple SecOps tasks, and use AI to do something, not all deliver the same time to value or leverage artificial intelligence to the same effect.
Determine how much work you want AI to take off your plate, the level of explainability you’re comfortable with, and how fast you need to see results.
Then, consider where you want to see your company in the next five years and choose the platform that best uses AI to hit that goal.
Katrina Thompson is an ardent believer in personal data privacy and the technology behind it. She is a freelance writer leaning into encryption, data privacy legislation, and the intersection of information technology and human rights.
