The Economics of Cybersecurity Are Broken
Cybersecurity has reached an inflection point. Three structural shifts, each individually significant and collectively decisive, have reshaped what it costs to defend an organisation and what it should cost.
The traditional pricing model for security tools, per GB of data ingested, per tool deployed, per feature enabled, was designed for an era when data volumes were manageable, threats arrived at human speed, and defence was a collection of point products. That era is over.
The question is no longer whether security pricing models will change. It is whether your organisation will be on the right side of that change.
Three Structural Shifts
Shift 1: The Telemetry Explosion
Security telemetry is growing exponentially. Every endpoint, cloud workload, identity provider, SaaS application, and network segment generates data that security teams must collect, normalise, correlate, and analyse. The attack surface is expanding faster than security budgets.
Under per-GB pricing, this creates a perverse incentive: the more visibility you need, the more you pay. Security teams are forced to make coverage trade-offs: reducing log retention, filtering out data sources, or limiting the scope of monitoring. This happens precisely when complete visibility is most critical.
The result is an engineered blind spot. Organisations pay more and see less.
Shift 2: The Disappearing Patch Window
The traditional vulnerability lifecycle (disclosure, patch development, testing, deployment) assumed organisations had weeks or months to respond. That assumption no longer holds.
A growing share of exploited vulnerabilities in 2026 are zero-days. Attackers are exploiting vulnerabilities before patches exist, before advisories are published, and before security teams even know the vulnerability is real. The time-to-exploit has collapsed from approximately one year in 2021 to 1.6 days in 2026 (source: zerodayclock.com).
Signature-based detection tools that charge per rule update, per threat feed, or per detection module cannot keep pace with threats that move faster than their update cycles. Organisations need detection that operates at the speed of the threat, and pricing that doesn’t penalise them for it.
Shift 3: Automated Exploitation at Machine Speed
The third shift is the most consequential. Attackers are automating the entire exploitation chain: reconnaissance, initial access, lateral movement, and data exfiltration. They use the same AI and automation technologies that defenders aspire to use.
Time-to-exploit milestones tell the story:
- 2021: ~1 year from disclosure to exploitation
- 2024: ~1 month
- 2025: ~1 week
- 2026: 1.6 days for critical vulnerabilities (source: zerodayclock.com)
Defence must now operate at machine speed. But under traditional pricing models, the automation and AI capabilities that enable machine-speed defence are sold as premium add-ons: separate SOAR licences, UEBA modules, and AI-powered investigation tools. Each carries its own pricing tier and consumption charges.
The tools that organisations need most are priced in a way that makes them least accessible.
The Unaffordable Security Gap
These three shifts converge to create what we call the unaffordable security gap: the widening divide between the security coverage an organisation needs and what it can afford under traditional pricing models.
The arithmetic is straightforward:
- Data volumes are growing at 25–40% per year
- Per-GB pricing means security costs grow at the same rate
- Security budgets grow at 5–15% per year
- The gap compounds annually, forcing increasingly severe coverage trade-offs
Traditional vendors respond to this gap by offering data filtering, tiered retention, and selective ingestion, tools that help organisations pay less by seeing less. This is the wrong answer to the right question.
The right answer is a pricing model that decouples cost from data volume entirely.
From Data Tax to Intelligence Credits
The solution is not to reprice the same model. It is to change what the pricing unit represents.
Traditional security pricing charges for inputs: bytes ingested, rules deployed, queries executed. Intelligence-credit pricing charges for outputs: threats detected, cases investigated, compliance maintained, incidents resolved.
SenseOn’s Flexible Intelligence Credit (FIC) model works like this:
1. Annual credit commitment, not per-GB metering
Organisations commit to an annual pool of intelligence credits sized to their environment. The more you commit, the lower the unit rate: from pay-as-you-go for flexible consumption down to significantly reduced rates at higher commitment tiers. The commitment is predictable and forecastable.
2. Credits consumed by outcomes, not data volume
Credits are drawn down when the platform delivers value across four pipelines:
- Detection & Response: Active threat analysis, correlation, and investigation across all telemetry sources
- Observability: Warm-tier retention for retrospective investigation and operational insight
- Compliance: Durable long-term storage for audit, legal, and regulatory requirements
- Resolve: Autonomous investigation and response: credits consumed only when AI completes a case, not when it attempts one
All four pipelines draw from the same credit pool. No separate licensing. No module gating.
3. AI efficiency rewards the customer
This is the critical inversion. Under per-GB pricing, vendor efficiency is irrelevant to the customer; you pay the same whether the platform processes your data well or poorly. Under intelligence credits, the more efficiently the AI works, the more outcomes you get per credit.
At 92.5% autonomous completion rate, Resolve resolves the vast majority of cases without human intervention. Each AI-accelerated resolution consumes credits based on severity (more complex threats consume more credits), but the customer only pays when the AI delivers a complete investigation. Human escalations, the safety net, cost nothing extra.
4. Compliance is a capability, not a surcharge
The compliance pipeline provides durable cold storage with retention up to 36 months and beyond, drawing from the same credit pool. NIS2, DORA, ISO 27001, PCI DSS: the storage needed to meet regulatory requirements is part of the platform, not a billing line item.
The Evidence: 30 Million Cases, 0.68% True Positives
The case for intelligence-credit pricing rests on a fundamental premise: that AI-powered detection can deliver outcomes that justify the model.
SenseOn’s cross-domain correlation, which cross-validates every alert using supervised learning, unsupervised anomaly detection, and deep-learning sequence analysis, has autonomously investigated over 30 million cases. Of those, 0.68% were true positives requiring human attention. The remaining 99.32% were resolved autonomously, eliminating the noise that consumes analyst time and drives alert fatigue.
This is not a theoretical capability. It is an operational reality that changes the economics of security operations:
- Alert volume reduction: From thousands of daily alerts to tens of confirmed threats
- Analyst productivity: Security teams focus on genuine threats, not false-positive triage
- AI-accelerated resolution: 92.5% of cases completed without human intervention
- Response speed: Machine-speed detection and investigation, not human-speed triage
When detection is this accurate and investigation this automated, the pricing model can shift from consumption (how much data did you process?) to outcome (how many threats did we detect and resolve?).
What This Means for Security Teams
The implications extend beyond the finance department.
For CISOs and security leaders: Intelligence-credit pricing transforms the budget conversation. Instead of defending ever-growing data costs, you present a predictable annual commitment tied directly to security outcomes. Board reporting shifts from cost justification to value demonstration.
For SOC teams: When the pricing model doesn’t penalise visibility, you can instrument everything. No more debates about which log sources to onboard. No more retention trade-offs. The AI handles the volume; your team handles the judgment calls.
For finance and procurement: Annual credit commitments are forecastable. No surprise invoices from data spikes. No complex true-up calculations. And as Resolve’s efficiency improves, you get more security per credit; the value increases over the contract term.
For compliance teams: The compliance pipeline draws from the same credit pool as detection. NIS2, DORA, ISO 27001, PCI DSS: the retention and audit capabilities are not premium add-ons. They’re capabilities you can turn on from your existing commitment.
The Architecture That Makes It Possible
Intelligence-credit pricing requires a different platform architecture. You cannot simply reprice a traditional SIEM; the underlying economics don’t work when every additional byte ingested increases marginal cost.
The architecture must satisfy three requirements:
- Single-agent telemetry collection: One lightweight sensor captures endpoint, network, and identity data. No separate EDR agent, NDR appliance, and SIEM collector, each with its own licensing and data-volume profile.
- AI-native detection across all pipelines: Detection is not a rule engine with ML bolted on. It is three AI methodologies (supervised, unsupervised, and deep learning) operating in concert, cross-validating every signal before generating an alert. The same intelligence fabric operates across Detection & Response, Observability, and Compliance pipelines.
- Autonomous investigation and response: Resolve doesn’t just detect; it investigates, correlates, and resolves. Human analysts are engaged for judgment, not for triage. And the commercial model reflects this: credits are consumed on completion, not on attempt.
This architecture produces a marginal cost structure where additional data within the same environment costs very little to process. The intelligence credit model passes that efficiency to the customer.
The Path Forward
The cybersecurity industry is at an inflection point. The structural shifts are irreversible. Data volumes will continue to grow. Zero-days will continue to dominate. Automation will continue to accelerate on both sides of the attack-defence divide.
Organisations face a choice: continue paying more for less under per-GB pricing, or adopt intelligence-credit models that align cost with the security outcomes that actually matter.
The transition starts with asking a simple question: am I paying for data, or am I paying for intelligence?
If the answer is data, the unaffordable security gap is already widening. Every year of exponential data growth under per-GB pricing compounds the problem. Every coverage trade-off creates a blind spot that attackers will find.
The alternative is a pricing model designed for the threat landscape as it actually exists, not as it existed when SIEM was invented.
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