Cybersecurity Has Reached an Inflection Point: Pricing Must Change

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 charges for outcomes, with data volume as one meter rather than the whole bill, and an architecture that shrinks the data before any storage is priced.

From Volume Pricing 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 Flex Intelligence Credit (FIC) model works like this:

1. Annual credit commitment, with data volume as one meter

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, with data volume one meter among several

Credits are drawn down across three data pipelines and the work of SenseOn's AI agents:

• Detection & Response: Active threat analysis, correlation, and investigation on the log sources you route to Detection & Response
• Observability: Warm-tier retention for retrospective investigation and operational insight
• Compliance: Durable long-term storage for audit, legal, and regulatory requirements
• Agentic Cybersecurity Team (ACT): The AI agents, such as Resolve, that investigate and resolve cases. Credits are used when an agent completes an outcome, not when it attempts one

All four 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.

Across all customer environments, 92.5% of cases are resolved by AI under human governance over a rolling 30-day window. The customer pays for the agents' activity when they deliver a completed outcome; this is not a charge for an MDR service. That is SenseOn's approach to human-AI teaming: the agents take the volume, people take the judgement calls.

4. Compliance is a capability, not a surcharge

The compliance pipeline provides durable, compressed cold storage, with retention configured per pipeline to match your requirements, 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 investigated more than 30 million cases over a rolling twelve-month window. Confirmed true positives run at 0.68% of those cases. Roughly 99 in every 100 things worth a look are not worth a human's look, and that noise is what 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: About 97% of alert noise is removed before an analyst sees a queue
• Analyst productivity: Security teams focus on genuine threats, not false-positive triage
• AI-accelerated resolution: 92.5% of cases resolved by AI under human governance, rolling 30-day window
• Response speed: Mean time to detect and respond under 20 minutes across managed-service customer environments

When detection is this accurate and investigation this automated, the pricing model can shift its weight from consumption (how much data did you process?) to outcome (how many threats did we detect and resolve?), with data volume as one meter, not the whole bill.

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 data volume is one meter rather than the whole bill, the debate about which log sources to onboard can start from security value. The AI handles the volume; your team handles the judgment calls.

For finance and procurement: Annual credit commitments are forecastable. No complex true-up calculations.

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:

1. 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.
2. AI-native detection on the sources you route to Detection & Response: 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. Detection runs on the sources you route to Detection & Response; the same Intelligence Fabric also serves the Observability and Compliance pipelines.
3. 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.

The cost saving comes from the same architecture. SenseOn's cost compression combines three levers: edge processing that removes unnecessary data before the pipeline; intelligent routing down three pipelines, configured by Weave, SenseOn's security engineering agent, with storage separated from compute; and ClickHouse compression, with storage priced on the compressed volume. Together these cut costs by about 40% against a traditional SIEM. The saving on SIEM costs alone would be larger, because SenseOn's price also includes the work of its Agentic Cybersecurity Team (ACT), the AI agents that investigate and resolve cases. You pay for the investigations the agents complete, not for an MDR service.

One distinction matters when you check the numbers. The published ~40% data reduction at the edge is a data-volume figure, measured as pre versus post processing. The ~40% saving against a traditional SIEM is a cost figure, and it comes from all three levers together.

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.

Related reading:

• The Hidden Costs of Your SIEM
• SIEM Alternatives: 7 Platforms That Replace Traditional SIEM
• Security Tool Consolidation: Consolidate 5 Tools into 1 Platform
• How SenseOn’s Cross-Domain Correlation Eliminates False Positives
• SenseOn vs Splunk: A Complete Comparison

Frequently asked questions

What are Flex Intelligence Credits?

Flex Intelligence Credits (FIC) are SenseOn's consumption unit. Organisations commit to an annual credit pool and pay for outcomes: detection and response across their data, analytics included, and the investigations SenseOn's AI completes. Data volume is one meter, not the whole bill. Credits are used when the platform delivers security outcomes: threats detected, cases investigated, telemetry retained for compliance, and cases resolved by SenseOn's AI agents. Storage is priced on compressed data. The more you commit, the lower the unit rate.

Why is traditional SIEM pricing broken?

Traditional SIEM pricing charges per GB of data ingested. As telemetry volumes grow exponentially and threats multiply, this model creates an 'unaffordable security gap' where organisations must reduce coverage to control costs; precisely when they need more visibility.

What are the three structural shifts in cybersecurity?

The three shifts are: (1) telemetry volumes growing exponentially as attack surfaces expand, (2) the disappearance of the patch window with a growing share of exploited vulnerabilities now being zero-days, and (3) attackers automating exploitation at machine speed, collapsing time-to-exploit from one year to 1.6 days.

How are SenseOn's AI agents charged?

SenseOn charges for the activity of its Agentic Cybersecurity Team (ACT), the AI agents that investigate and resolve cases, not for an MDR service. Credits are used when an agent completes an outcome.

How much can SenseOn cut costs compared with a traditional SIEM?

About 40%. The saving comes from three levers: edge processing that removes unnecessary data before the pipeline; intelligent routing down three pipelines, configured by Weave, SenseOn's security engineering agent, with storage separated from compute; and ClickHouse compression, with storage priced on the compressed volume. That ~40% is a cost figure; it is not the same as the separate ~40% reduction in data volume at the edge. The saving on SIEM costs alone would be larger, because SenseOn's price also covers the work of its AI agents.

What is the unaffordable security gap?

The unaffordable security gap describes the widening divide between the security coverage organisations need and what they can afford under traditional per-tool, per-feature, per-dataset pricing. As threats accelerate, the cost of adequate defence under legacy models grows faster than security budgets.

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