AI Priced by Productivity, Not Promises
Agentic
AI Priced by Productivity, Not Promises
Turning AI Into Measurable, Governed Outcomes for Security Operations
By Simon Hunt, Chief Product Officer, Securonix
Security operations teams are facing a structural problem that tools alone cannot solve.
Alert volumes continue to rise. Telemetry is fragmented across cloud, SaaS, identity, and endpoint environments. Experienced analysts are stretched thin, and hiring additional talent has become increasingly difficult. At the Same time, boards are demanding faster response, stronger governance, and clear proof that security investments are delivering measurable value.
AI is often presented as the answer. Yet many SOC leaders hesitate to trust AI in production.
Their concerns are justified.
Most AI initiatives fail in the SOC not because the models are weak, but because the operating and economic models around them are broken. When AI decisions cannot be explained, audited, or tied to measurable productivity, trust erodes. When AI cost scales unpredictably with data volume or usage mechanics, boards lose confidence. And when automation lacks governance, it stalls before it becomes operational.
At Securonix, we started with a different question.
How do we modernize the SOC in a way that scales analyst capacity, governs AI by design, and proves value in terms security leaders and boards actually care about?
The answer is the Productivity-Based Agentic SOC.
Why the SOC Operating Model Is Breaking
Most SOCs do not struggle because they lack technology. They struggle because the work itself does not scale.
Analysts spend hours triaging alerts, stitching together context, and preparing investigations before meaningful response can begin. As environments grow more complex, this manual coordination becomes the bottleneck. Adding more tools increases noise. Adding more data increases cost. Adding more analysts rarely keeps pace.
At the same time, traditional SIEM economics make the problem worse. Pricing based on raw data volume treats all telemetry the same, regardless of its analytical value. As data grows, cost grows faster than outcomes. Security leaders are forced to choose between visibility and budget, and AI becomes another unpredictable expense rather than a force multiplier.
The result is a SOC that works harder every year while delivering diminishing returns.
Fixing this requires more than automation. It requires a new operating and economic model.
Introducing the Productivity-Based Agentic SOC
The Productivity-Based Agentic SOC redefines how security operations scale, govern AI, and prove value.
It shifts the SOC away from data-volume and consumption-based cost models and toward measurable analyst productivity and outcome-driven data economics. Instead of paying more as data grows, organizations invest in outcomes. Instead of licensing AI as opaque features, they measure the work AI performs on behalf of analysts.
This model is built on three tightly integrated foundations:
- Outcome-driven data economics, which align data cost to analytical value rather than raw volume
- Productivity-based AI, which prices AI by verified analyst work delivered
- Governed agentic operations, which embed human oversight, explainability, and auditability into every AI-assisted action
Together, these elements transform AI from experimentation into a trusted, board-ready operating model for the SOC.
Data as the Foundation: Outcome-Driven Data Economics
Agentic AI is only as effective as the data it operates on, and only as scalable as the economics behind that data.
The Productivity-Based Agentic SOC is built on Securonix Unified Defense SIEM, which provides a normalized security telemetry and behavioral data model across identity, endpoint, network, cloud, SaaS, application, and data sources.
Supporting this foundation is Data Pipeline Manager with Flex Consumption, which aligns data handling and cost to the purpose the data serves.
High-value telemetry is prioritized for real-time detection and response. Investigation and threat hunting data is handled through enriched, cost-efficient pipelines. Compliance and long-term retention data is stored without inflating analytics costs.
This approach eliminates the historical tradeoff between visibility and spend. Organizations can ingest more data, retain it longer, and support AI-driven operations without runaway SIEM costs.
Data decisions are driven by risk and outcome, not pricing mechanics.
Agentic Mesh: The Operating Layer for AI at Scale
To turn outcome-driven data into measurable productivity, Securonix introduced the Agentic Mesh.
Agentic Mesh is not a single AI feature or a copilot. It is a coordination layer embedded within Unified Defense SIEM that orchestrates specialized AI agents across detection, investigation, response, and reporting.
Rather than relying on a monolithic AI engine, Securonix deploys purpose-built agents, each designed for a specific SOC task. The Mesh ensures these agents share context, persist investigations across stages, and operate within defined policy guardrails.
This separation of orchestration from execution avoids black-box automation and enables scalable, governed AI workflows.
At the center of the Agentic Mesh is Sam, the AI SOC Analyst.
Sam: The AI SOC Analyst and Unit of Productivity
Sam is the AI SOC Analyst and the primary interface between human analysts and the agent ecosystem.
Sam does not replace analysts. It absorbs repetitive Tier 1 and Tier 2 investigation work, coordinates specialized agents, and presents explainable narratives that allow analysts to make faster, higher-confidence decisions.
Within the Agentic Mesh, Sam orchestrates agents such as:
- Noise Control Agent to reduce alert fatigue
- Search Agent to translate natural language questions into optimized queries
- Investigation Agent to correlate telemetry and threat intelligence
- Response Agent to prepare policy-approved containment actions
- Data Pipeline Agent to optimize data routing and cost
- Threat Briefing Agent to generate clear, executive-ready summaries that translate technical activity into business risk.
Analysts are no longer forced to pivot between tools or manually stitch context together. They receive clearer cases, stronger narratives, and fewer distractions.
The result is not just faster response, but more consistent, defensible decisions.
Measuring AI Value in Operational Terms
One of the most persistent frustrations SOC leaders express is that traditional AI pricing does not reflect real value.
Charging for AI based on tokens, usage, seats, or data volume says nothing about productivity, risk reduction, or resilience.
The Productivity-Based Agentic SOC measures AI value in terms leaders actually understand:
- Analyst time absorbed by AI
- Reduction in manual investigation effort
- Faster detection and response
- Improved consistency and quality of outcomes
Sam is priced based on verified analyst productivity delivered, measured in minutes of investigation, triage, enrichment, and reporting performed by AI. This creates a transparent link between AI investment and operational impact.
Because all activity runs through Unified Defense SIEM, these outcomes are measurable, auditable, and defensible. CISOs can clearly demonstrate ROI to finance teams, auditors, and boards.
Built for Governance, Trust, and Real-World Use
AI only becomes operational when trust is earned.
The Productivity-Based Agentic SOC is designed with governance built in, not bolted on. Every AI-assisted action operates under human-in-the-loop control. Policies enforce scope, escalation rules, and separation of duties. All actions are logged, explainable, and reversible.
AI operates inside the customer’s environment, using their data, their policies, and their risk tolerance.
This makes the model suitable for regulated and risk-averse environments, where explainability and accountability are non-negotiable.
Closing the Skills Gap with Agentic AI Academy
Technology alone does not modernize a SOC. People do.
As AI becomes operational, analysts must know how to supervise it, validate its outputs, and apply human judgment where it matters most. Without enablement, even the best AI will remain underutilized.
That is why Securonix introduced Agentic AI Academy.
Agentic AI Academy is a hands-on enablement program designed to help SOC teams operationalize agentic AI responsibly and effectively. Analysts learn how to supervise AI-powered investigations, guide agent behavior through feedback, and make confident escalation and response decisions using explainable outputs.
Rather than replacing expertise, the Academy accelerates it, helping teams move from reactive alert handling to strategic threat analysis.
A Practical Path Forward
The Productivity-Based Agentic SOC is not about unchecked autonomy. It is about governed intelligence at scale.
For SOC teams, it means less noise, faster investigations, and reclaimed analyst capacity.
For security leaders, it means predictable economics, measurable productivity, and defensible AI governance.
For boards, it means clarity, accountability, and trust.
This is how security operations become Breach Ready and Board Ready in an agentic world.
