This week, Securonix introduced Sam, the AI SOC Analyst, and Agentic Mesh in collaboration with Amazon Web Services. The headline is not another AI feature. It is a shift to a Productivity-Based AI Model.
Ever watched your SOC team drown in alerts while the board asks for “clear AI ROI”?
Picture this.
It’s 8:45 a.m. The CISO joins a board pre-brief. Overnight alerts crossed 40,000. Two analysts called in sick. A regulator requested evidence of AI governance. Finance wants justification for rising SIEM spend.
The team uses AI. But they cannot prove what it actually delivered.
This is the gap Securonix is targeting with its latest launch in collaboration with Amazon Web Services. The company introduced Sam, the AI SOC Analyst, and the Securonix Agentic Mesh—alongside a productivity-based AI model for security operations.
For CX and EX leaders, this is not just cybersecurity news. It’s a blueprint for governed AI at scale.
What Is a Productivity-Based AI Model—and Why Does It Matter?
A productivity-based AI model measures AI by work completed, not by usage or data consumed.
Most enterprise AI pricing tracks tokens, storage, or features. That model rewards consumption. It rarely proves outcomes.
Securonix flips this logic.
Sam is licensed based on verified analyst-equivalent work completed by AI. Productivity is tracked transparently. Leaders can quantify hours saved and throughput gained.
For CX and EX leaders, this reframes AI value:
- From feature adoption → to measurable output
- From experimentation → to governed production
- From innovation theater → to board-ready ROI
This shift mirrors what CX leaders face with journey AI and copilots. The board doesn’t want chatbot usage stats. It wants deflection rates, resolution time reduction, and cost-to-serve improvement.
Security is now speaking the same language.
What Is Sam, the AI SOC Analyst?
Sam is a governed, always-on digital SOC teammate that automates Tier 1 and Tier 2 work inside the Unified Defense SIEM.
Sam performs:
- Alert triage
- Investigation enrichment
- Correlation analysis
- Response preparation
- Reporting summaries
It operates natively inside Securonix’s platform. Analysts remain in control through human-in-the-loop oversight.
Many AI copilots assist. Few operate as structured systems of work. Sam orchestrates specialized AI agents across investigation steps. It presents plain-language summaries analysts can validate or escalate.
The result: AI augments judgment. It does not replace it.
Why Are SOCs Struggling with AI Governance?
Because most AI deployments scale faster than control frameworks.
Security leaders face three tensions:
- Alert volume keeps rising.
- Analyst shortages persist.
- Regulators demand explainability.
Boards now ask harder questions:
- Is AI governed?
- Can actions be audited?
- Are policies enforced?
- Can decisions be reversed?
Unstructured AI cannot answer these.
That’s where the Securonix Agentic Mesh enters.
What Is Agentic Mesh and How Is It Different?
Agentic Mesh is a governed orchestration layer coordinating specialized AI agents across detection, investigation, response, and reporting.
Unlike monolithic assistants, Agentic Mesh functions as a system of work.
It:
- Maintains shared context across agents
- Enforces enterprise policy guardrails
- Ensures actions are explainable and auditable
- Allows reversibility and human validation
Built using Amazon Bedrock AgentCore, it runs securely within customer environments. That provides enterprise-grade isolation and resiliency.
Copilots answer questions.
Agentic systems complete governed workflows.
That distinction changes enterprise AI maturity.
How Does This Translate into Board-Ready Outcomes?
Security leaders increasingly operate under board scrutiny. AI must prove trust, not promise it.
According to Sameer Ratolikar, CISO at HDFC Bank:
“In a regulated financial environment, AI must earn trust through transparency and control. With Securonix, we are using AI agents to reduce noise, accelerate investigations through natural-language search, and prepare response actions, all while keeping our analysts firmly in control.”
Simon Hunt, Chief Product Officer at Securonix, frames the challenge clearly:
“We built Sam and Agentic Mesh to solve two problems CISOs face every day: unscalable workloads and unprovable AI value.”
For board conversations, productivity-based AI enables:
- Quantified analyst-equivalent work
- Clear cost avoidance narratives
- Controlled AI action logging
- Regulatory-ready explainability
What Is DPM Flex and Why Does Data Economics Matter?
DPM Flex routes telemetry based on analytical value rather than raw volume to control SIEM costs.
AI productivity collapses if data costs spiral.
Data Pipeline Manager with Flex Consumption (DPM Flex) introduces outcome-driven data economics. Instead of ingesting everything, it prioritizes high-value telemetry.
For CX parallels:
- Don’t feed every interaction into premium AI models.
- Route low-risk flows differently.
- Align data ingestion with measurable outcomes.
Cost governance is part of AI governance.
Key Insights for CX and EX Leaders
1. Measure AI by work completed.
Adoption metrics mean little without output metrics.
2. Embed governance inside the system.
Retroactive compliance is fragile.
3. Protect human oversight.
AI scales best when it augments judgment.
4. Align AI with financial narratives.
Boards approve outcomes, not experimentation.
5. Control data economics early.
Scaling AI without cost discipline creates backlash.
Common Pitfalls in Enterprise AI Adoption
- Launching AI pilots without outcome KPIs
- Treating governance as a later phase
- Measuring usage instead of throughput
- Ignoring explainability requirements
- Scaling data ingestion without ROI mapping
These pitfalls create fragmentation. They erode executive confidence.
A Practical Framework: The PRODUCT Model for Governed AI
CXQuest proposes the PRODUCT Model for enterprise AI scaling:
P – Productivity Units Defined
Define measurable work equivalents.
R – Risk Guardrails Embedded
Enforce policy inside workflows.
O – Oversight Maintained
Keep humans in control of escalation.
D – Data Economics Managed
Align ingestion with analytical value.
U – Use Case Boundaries Clear
Start with defined, high-volume work.
C – Context Shared Across Agents
Avoid siloed AI assistants.
T – Transparent Reporting to Leadership
Translate output into financial language.
Securonix operationalizes many of these principles inside security operations. CX teams can adapt the same structure.
How Does This Impact Employee Experience (EX)?
Analyst burnout mirrors contact center fatigue.
Repetitive triage work drives attrition.
Lack of visibility into impact reduces engagement.
By absorbing Tier 1 and Tier 2 noise, Sam allows analysts to focus on higher-risk judgment calls.
AI should remove drudgery, not autonomy.
Productivity-Based AI Model: Why This Announcement Signals a Broader Market Shift
Security often pioneers governance frameworks before CX adopts them.
The move toward agentic AI orchestration suggests the next enterprise AI phase will focus on:
- Governed autonomy
- Workflow-level AI
- Productivity-based pricing
- Explainability-first design
Boards will increasingly ask:
How much work did AI complete?
Was it controlled?
Can we defend it?
This model answers those questions directly.
FAQ
How is productivity-based AI different from traditional AI pricing?
It ties cost to verified work completed rather than data usage or features.
What does “agentic” mean in enterprise AI?
It refers to AI systems that coordinate specialized agents to complete structured workflows.
How does human-in-the-loop oversight work?
Analysts review, validate, or reverse AI-generated actions before execution.
Why do boards care about AI governance in SOCs?
Security failures carry regulatory and financial risk. AI decisions must be explainable.
Can this model apply to CX environments?
Yes. Any high-volume, rule-driven workflow can adopt productivity-based AI measurement.
Actionable Takeaways for CX and Security Leaders
- Define one workflow where AI can complete measurable units of work.
- Quantify analyst or agent time saved per completed unit.
- Embed policy guardrails before scaling AI access.
- Implement human review for high-risk actions.
- Build dashboards translating AI output into financial impact.
- Align data ingestion with outcome-driven analytics.
- Present AI ROI in board language, not technical metrics.
- Audit AI workflows quarterly for governance integrity.
Sam, the AI SOC Analyst, Agentic Mesh, and DPM Flex are available globally for Securonix customers.
The deeper shift is clear.
AI must do real work.
It must be governed by design.
And its value must stand up in the boardroom.
