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The Hidden Cost of Staying Safe
Cybersecurity has reached an uncomfortable turning point. Organizations are spending more than ever on security, yet many still feel dangerously exposed. The problem is no longer simply a lack of security tools, security professionals, or awareness. Increasingly, it is the economics of cybersecurity itself.
A major breach can now cost millions of dollars, while global cybersecurity spending continues to climb toward extraordinary levels. For large enterprises, those numbers are painful but potentially manageable. For small and medium-sized businesses, the same numbers can be existential.
The result is a growing contradiction: companies cannot afford to ignore cybersecurity, but many cannot afford the way cybersecurity is currently being delivered.
That tension creates what could become one of the industry’s most important challenges of the next decade: a cybersecurity affordability crisis.
The Night Every Security Leader Fears
Imagine receiving a phone call at 2 a.m.
A ransomware group has broken into the
The security team immediately begins containment.
Lawyers are called. Customers may need to be notified. Regulators could become involved. Forensic investigators arrive. Insurance providers start asking questions. Meanwhile, every hour of downtime increases the financial damage.
This scenario is no longer unusual.
Breaches Are Becoming Financial Events
Ransomware is only one piece of the problem. Business email compromise, credential theft, cloud attacks, insider threats and third-party supply-chain compromises can all create enormous costs.
According to the article,
That figure illustrates the fundamental problem facing businesses.
Cybersecurity is not an optional IT expense anymore. It is a business-continuity expense.
Cybersecurity Spending Is Surging
At the same time, organizations are spending enormous amounts trying to prevent these incidents.
Gartner projects worldwide cybersecurity spending to reach approximately $239.8 billion, compared with $193.4 billion in 2024, according to the original article.
The increase makes sense on the surface.
More attacks mean more protection.
More cloud services mean more monitoring.
More employees working remotely mean more endpoints.
More artificial intelligence means more data and infrastructure to secure.
But there is a catch.
More Spending Does Not Automatically Mean More Security
Security budgets can grow while security outcomes remain stagnant.
A company might purchase another vulnerability scanner, another endpoint platform, another cloud security product and another AI-powered detection system.
Yet if all those systems generate thousands of alerts that nobody has time to investigate, the organization may have purchased more information rather than more protection.
This is where cybersecurity economics becomes complicated.
SMBs Are Carrying the Heaviest Burden
Large companies can employ security operations centers, dedicated incident response teams, threat intelligence specialists, vulnerability management teams and full-time security leadership.
A small business might have one IT administrator.
Sometimes that person is also responsible for servers, employee laptops, Microsoft 365, backups, networking, software deployment and compliance.
Expecting the same organization to operate a sophisticated security stack is unrealistic.
The Weakest Link Can Affect Everyone
The problem extends beyond individual businesses.
Small and medium-sized companies are deeply connected to larger organizations through supply chains.
A manufacturer may depend on dozens of smaller suppliers.
A hospital may depend on technology vendors.
A large retailer may depend on logistics providers.
A financial institution may rely on software companies and external service providers.
If one smaller organization is compromised, attackers may use that foothold to reach a much larger target.
Cybersecurity affordability therefore becomes a systemic security problem.
The Economics of the Security Industry
Bryson Byrd, a cybersecurity advisor at Huntress, argues that cybersecurity vendors face their own economic pressures.
Venture-backed companies naturally seek large, profitable markets.
Enterprise customers can afford expensive platforms, large contracts and complex security deployments.
SMBs often cannot.
That creates a market incentive to build sophisticated products around the needs of large organizations.
The unintended consequence is that smaller organizations can become underserved.
Enterprise Security Does Not Always Scale Down
An enterprise security platform might assume the customer has a dedicated security engineering team.
It might require several analysts to configure and maintain.
It might generate thousands of findings that need professional interpretation.
It might charge according to users, endpoints, data volume or events.
For a multinational corporation, that may be acceptable.
For a company with 50 employees, it can be financially impossible.
The Real Problem Is Not Simply “Spend More”
The natural response to rising cyber threats is often to increase the security budget.
But there is a limit.
Companies cannot endlessly increase spending while simultaneously dealing with higher cloud costs, AI expenses, staffing shortages, compliance requirements and economic uncertainty.
At some point, security leaders must answer a much harder question:
Which security investments actually reduce the risks that matter most?
Tool Sprawl Is Becoming a Financial Problem
Syed Ghayur of ArmorCode highlights another major problem: security-tool sprawl.
According to the article, the average enterprise may operate around 40 security scanners, while broader security environments can contain approximately 83 tools from 29 vendors.
Every tool creates costs.
There is the purchase price.
There is deployment.
There is integration.
There is maintenance.
There is training.
There are licenses.
And there is the human cost of interpreting what the tools discover.
The Duplicate Alert Problem
Imagine five security products identifying essentially the same weakness.
The organization may receive five alerts.
A dashboard may report five findings.
Management may interpret that as five separate security problems.
But operationally, there may only be one underlying exposure.
This is why measuring cybersecurity success by the number of findings can be misleading.
A company with 100,000 findings is not necessarily less secure than one with 10,000.
The important question is which findings represent meaningful risk.
AI Was Supposed to Help
Artificial intelligence entered cybersecurity with an enormous promise.
AI could summarize alerts.
AI could analyze vulnerabilities.
AI could write detection rules.
AI could investigate suspicious activity.
AI could generate remediation recommendations.
In theory, AI should allow smaller teams to accomplish more.
But the economics are proving more complicated.
AI Can Create Another Bill
AI services often have their own costs.
Organizations may pay for premium models, API usage, tokens, additional context windows, specialized security products and infrastructure.
If AI is used indiscriminately against every vulnerability and every alert, costs can increase rapidly.
The irony is uncomfortable.
Companies may introduce AI to reduce security workload, only to discover that they have created another expensive layer of infrastructure.
More Automation Can Also Create More Risk
Automation is powerful, but cybersecurity is not a simple factory process.
A vulnerability in a public-facing payment server is not equivalent to the same vulnerability in an isolated development machine.
A critical issue affecting an internet-facing database deserves a different response from a low-risk issue inside an inaccessible test environment.
Automation that treats everything equally can therefore become counterproductive.
AI-Generated Security Fixes Need Human Oversight
The original article cites research from 1Password suggesting that large-language-model-generated patches failed to properly resolve vulnerabilities, introduced new vulnerabilities, or did both in 53.9% of cases on average.
That does not mean AI has no place in software security.
It means organizations need to distinguish between AI-assisted remediation and AI-trusted remediation.
Those are very different concepts.
The Dangerous Choice Between Two Extremes
Ghayur identifies two problematic reactions.
One is spending indiscriminately.
The organization buys more tools and hires more people simply because the number of alerts continues increasing.
The other is automating indiscriminately.
The company tries to eliminate human involvement because it wants to reduce expenses.
Both strategies can fail.
The first becomes financially unsustainable.
The second can create dangerous security decisions.
The Better Metric Is Risk Reduction
The cybersecurity industry has traditionally measured activity.
How many vulnerabilities were discovered?
How many alerts were generated?
How many endpoints were scanned?
How many patches were deployed?
Those numbers are useful, but they do not necessarily tell executives whether the organization is becoming safer.
The better question is:
How much meaningful risk did we remove?
Not Every Vulnerability Deserves Equal Attention
A vulnerability on an internet-facing production server should usually receive more attention than an identical vulnerability on an isolated laboratory machine.
Context matters.
Asset importance matters.
Exploitability matters.
Exposure matters.
Business impact matters.
Attack activity matters.
Data sensitivity matters.
This is why modern vulnerability management needs to move from finding management to risk management.
Healthcare Shows Why This Matters
Healthcare organizations are especially vulnerable to this economic problem.
Hospitals operate enormous technology environments containing legacy systems, medical devices, specialized applications and sensitive patient information.
Replacing everything is unrealistic.
Protecting everything equally is also unrealistic.
Security teams therefore have to prioritize.
The same principle applies to manufacturing, financial services, government and critical infrastructure.
The Cybersecurity Budget Needs a Strategy
The article references recommendations that organizations allocate approximately 8% to 12% of their IT budgets to cybersecurity, with healthcare, financial services and government potentially targeting 10% to 15%.
But percentages alone do not solve the problem.
A company can spend 15% of its IT budget inefficiently.
Another organization can spend less while achieving stronger risk reduction through better architecture, prioritization and automation.
Budget size matters.
Budget efficiency matters more.
Cybersecurity Needs Its Own Version of FinOps
One of the most interesting ideas in the article is applying the principles of FinOps to cybersecurity.
FinOps emerged because organizations discovered that cloud computing did not automatically make IT cheaper.
Cloud resources could be created quickly, but that convenience could also produce runaway spending.
Visibility and governance became essential.
Cybersecurity is facing a similar transformation.
Security Costs Need Visibility
Organizations need to know exactly what they are paying for.
Which tools overlap?
Which products are actually being used?
Which licenses are unnecessary?
Which alerts are duplicates?
Which vulnerabilities matter?
Which AI workloads are generating measurable security value?
Which security controls are reducing real-world exposure?
Without those answers, security spending becomes difficult to defend.
The Security Leader Is Becoming a Business Strategist
The modern CISO cannot simply say:
We need another security platform.
The more important conversation is:
“We have this specific business risk. This investment reduces it by this amount. Here is what happens if we do nothing.”
That language connects cybersecurity with business resilience.
It also makes security easier for executives and boards to understand.
Cybersecurity Affordability Is About Resilience
The goal should not be to make cybersecurity cheap.
Cheap security can become expensive security after an attack.
The goal is to make cybersecurity economically sustainable.
A sustainable security program protects the most important assets first, reduces unnecessary complexity and uses automation where it is safe.
Small Businesses Need Simplicity
SMBs do not necessarily need 50 different security products.
They need dependable fundamentals.
Strong identity protection.
Multi-factor authentication.
Reliable backups.
Endpoint protection.
Secure email.
Patch management.
Network segmentation where appropriate.
Centralized logging.
Incident response preparation.
Employee security awareness.
And perhaps most importantly, a security strategy that someone actually has time to manage.
The Future May Belong to Consolidated Platforms
The affordability crisis could accelerate consolidation.
Instead of buying dozens of specialized tools, organizations may increasingly prefer platforms that combine endpoint security, identity monitoring, vulnerability management, detection and response.
That does not mean every security tool will disappear.
Specialized products will remain important.
But organizations will increasingly ask whether another standalone tool is worth the operational burden.
Security Vendors Will Face New Pressure
Vendors may also need to rethink pricing.
Per-user pricing can become painful.
Per-event pricing can discourage monitoring.
Per-data pricing can punish organizations that generate large amounts of telemetry.
AI token-based costs introduce another variable.
The winning vendors may increasingly be those that can demonstrate measurable outcomes instead of simply selling more alerts.
Deep Analysis
Measuring Risk Instead of Noise
A mature security program should prioritize vulnerabilities according to actual business exposure.
A simple conceptual scoring model might look like:
Risk = Exposure × Exploitability × Asset_Impact × Threat_Activity
This is not a universal industry-standard formula, but it demonstrates the principle.
A critical vulnerability on an isolated machine may deserve less immediate attention than a medium-severity vulnerability exposed directly to the internet.
Finding Internet-Facing Assets
Security teams can begin with basic asset discovery.
For authorized Linux environments:
sudo ss -tulpn
This shows listening services and can help identify unexpectedly exposed applications.
For local network discovery in an authorized environment:
nmap -sV 192.168.1.0/24
Only scan networks and systems you own or have explicit permission to test.
Checking for Outdated Packages
On Debian or Ubuntu systems, administrators can review available package updates with:
apt list --upgradable
For Red Hat-based systems:
dnf check-update
The objective is not simply to patch everything blindly.
Teams should identify which outdated components are exposed, exploitable and business-critical.
Searching Security Logs
Linux administrators can quickly inspect recent authentication activity with:
sudo journalctl -u ssh --since "24 hours ago"
This can help identify unusual authentication activity.
Again, the important step is not collecting logs for their own sake.
It is connecting security events to meaningful risk.
Checking Disk Encryption and System Security
A basic Linux environment can be inspected with:
lsblk -f
and:
sudo systemctl --failed
These commands do not constitute a security audit, but they demonstrate a broader principle: security teams should continuously understand what their infrastructure is actually doing.
Vulnerability Prioritization Logic
Organizations can use a simple workflow:
1. Discover asset
2. Identify vulnerability
3. Determine internet exposure
4. Determine business importance
5. Check exploitation activity
6. Assess available mitigation
7. Prioritize remediation
8. Verify the fix
9. Reassess remaining risk
This approach is more sustainable than simply sorting vulnerabilities by severity.
AI Should Assist the Decision
AI can be useful for summarizing large amounts of security information.
For example:
Input:
10,000 vulnerability findings
AI:
– Group duplicates
– Identify affected assets
– Map business context
– Highlight internet-facing systems
– Identify known exploitation
– Recommend remediation order
Human:
– Validate priorities
– Approve remediation
– Assess business impact
That division of responsibility is much safer than allowing an AI system to autonomously modify thousands of production systems.
Automation Needs Guardrails
Organizations should establish approval thresholds.
For example:
Low Risk -> Automated remediation Medium Risk -> Automated recommendation + human approval High Risk -> Security + system owner approval Critical Production Risk -> Incident/change-management process
The exact thresholds should depend on the environment.
The principle is what matters.
Automation should accelerate good decisions, not eliminate judgment.
Measuring Security ROI
A security team could track metrics such as:
Mean Time to Detect
Mean Time to Respond
Mean Time to Remediate
Critical Exposure Reduction
Internet-Facing Asset Reduction
MFA Coverage
Backup Recovery Success
Privileged Account Reduction
Security Tool Utilization
AI Cost per Resolved Risk
These metrics are more meaningful than simply reporting how many alerts were generated.
The Economics of Every AI Token
AI spending should eventually be connected to outcomes.
A security team should be able to ask:
AI Cost
↓
Security Task
↓
Time Saved
↓
Risk Reduced
↓
Business Value
If an AI workflow costs thousands of dollars but produces little measurable improvement, it should be redesigned.
Security Tool Consolidation
Organizations should periodically ask:
Do we still need this tool?
Does another platform already provide this capability?
How many employees actively use it?
What risks does it reduce?
What happens if we remove it?
Unused security tools represent more than wasted licensing money.
They also create operational complexity.
The SMB Opportunity
The affordability crisis could create a major opportunity for managed security providers.
Small businesses may not need to build their own 24/7 security operations center.
Instead, they can potentially use managed detection and response, managed identity protection, managed backups and other services.
The key requirement is that those services remain affordable and transparent.
The Bigger Security Equation
The cybersecurity industry has spent years increasing the amount of data it collects.
The next phase should focus on increasing the amount of useful security decisions generated from that data.
That is a fundamental shift.
More telemetry is not automatically more security.
More scanners are not automatically more security.
More AI is not automatically more security.
More spending is not automatically more security.
Better prioritization is security.
What Undercode Say:
The Real Crisis Is Complexity
The cybersecurity affordability crisis is not simply about rising prices.
It is about rising complexity.
Every additional tool creates another dashboard, another integration and another source of alerts.
At some point, the security team becomes overwhelmed by its own defensive infrastructure.
SMBs Are Strategically Important
Protecting large enterprises while leaving thousands of smaller companies vulnerable creates an unstable ecosystem.
Attackers understand supply chains.
They do not necessarily attack the biggest company first.
Sometimes they attack the easiest company.
Security Needs an Economic Reset
The industry has traditionally rewarded discovery.
Find more vulnerabilities.
Generate more alerts.
Deploy more sensors.
The future should reward measurable risk reduction.
That is a very different business model.
AI Will Not Automatically Make Security Cheaper
AI can reduce repetitive work.
It can also increase spending.
Organizations need governance around model selection, token usage, data processing and automation.
Otherwise, AI becomes another source of tool sprawl.
Human Judgment Still Matters
Security decisions frequently depend on context.
An AI model can recognize patterns.
A security professional understands the
The best systems will combine both.
Consolidation Could Accelerate
The affordability crisis may push organizations toward integrated platforms.
Companies will increasingly ask vendors to reduce complexity rather than add another layer.
Security Vendors Must Prove Value
A security product should eventually answer a simple question:
What risk does this product actually remove?
If the answer is unclear, its value becomes difficult to defend.
CISOs Need Financial Fluency
Modern security leaders will increasingly need to understand budgets, operational costs, business risk and return on investment.
Cybersecurity is becoming inseparable from corporate finance.
The Boardroom Conversation Is Changing
Instead of reporting thousands of vulnerabilities, CISOs will increasingly need to report how many critical exposures remain.
That is a much more useful executive metric.
Resilience Beats Perfection
No organization can eliminate every vulnerability.
The realistic goal is to prevent catastrophic exposure and recover quickly when something goes wrong.
Backups Are Still Security
In an AI-driven cybersecurity market, basic controls remain incredibly important.
A reliable, tested backup can sometimes provide more practical resilience than another expensive dashboard.
Identity Is Central
Strong authentication and privileged-access controls can reduce enormous amounts of attack surface.
Organizations should not allow flashy technologies to distract from fundamentals.
Visibility Must Become Actionable
Knowing that a vulnerability exists is only the beginning.
Security teams need to know whether it is exploitable, exposed and important.
Risk-Based Security Is More Sustainable
Risk-based prioritization allows smaller teams to concentrate resources where they matter most.
That is exactly what an affordability-focused security strategy should accomplish.
The Number of Tools Should Not Be a Status Symbol
A company running 80 security tools is not necessarily safer than one running 20.
The quality of implementation matters more than the size of the tool inventory.
AI Needs Financial Governance
Security teams should track how much AI costs per workflow, investigation and remediation.
Otherwise, AI spending can quietly grow alongside vulnerability volume.
Automation Should Be Selective
Automating low-risk repetitive tasks makes sense.
Automatically modifying critical production systems based solely on AI recommendations is much harder to justify.
The Supply Chain Is the Real Battlefield
Large companies cannot fully protect themselves if their suppliers remain severely underprotected.
Cybersecurity therefore needs to become an ecosystem responsibility.
SMB Security Should Be Simpler
Small businesses need practical protection rather than enterprise-grade complexity.
The best SMB security solution may be the one that requires the least specialized expertise to operate correctly.
Managed Security Will Become More Important
Outsourcing specialized security operations could become increasingly attractive for companies that cannot afford full-time security teams.
Security Budgets Will Face More Scrutiny
As economic pressure increases, security leaders will have to justify every major investment.
That could ultimately improve the industry.
Waste Will Become Easier to Identify
Duplicate products, unused licenses and ineffective security controls are likely to face greater scrutiny.
Vendors Will Need Better Evidence
Security marketing alone will become less persuasive.
Customers will increasingly want measurable outcomes.
AI Security Economics Will Mature
The current AI boom is still relatively young.
Organizations are experimenting.
Eventually, they will compare AI costs with actual security improvements.
The Best Security Stack May Be Smaller
A carefully integrated security environment can outperform a massive collection of disconnected tools.
Security Teams Need Fewer False Priorities
Every unnecessary alert consumes human attention.
Attention is one of the most expensive resources in cybersecurity.
Vulnerability Counts Can Be Misleading
A large vulnerability number can frighten executives without explaining what actually matters.
Risk context is more valuable than raw volume.
Cybersecurity Is Becoming a Resilience Discipline
Security is increasingly about keeping the business functioning despite attacks.
That requires preparation, recovery and prioritization.
The Economics Could Reshape the Industry
If cybersecurity continues becoming more expensive, organizations will demand more efficient products.
That pressure could trigger consolidation and innovation.
The SMB Market Could Become More Attractive
Vendors that create affordable, automated and genuinely usable security products for smaller businesses could address one of the industry’s largest structural gaps.
Security Should Be Treated as Infrastructure
Businesses should think of cybersecurity like electricity or network connectivity.
It is fundamental to operations.
But that does not mean organizations should waste resources on inefficient infrastructure.
AI Should Reduce Work, Not Multiply It
If AI creates five new dashboards and three new workflows for every problem it solves, something has gone wrong.
Cybersecurity Needs a Cost Culture
Security teams have historically focused heavily on risk.
They now need to understand cost as well.
The objective is not minimizing spending.
It is maximizing protection per dollar.
The Future Will Reward Prioritization
The organizations that understand their most important assets, exposures and attack paths will be better positioned than organizations that simply collect more security data.
The Biggest Lesson
The cybersecurity affordability crisis is ultimately a warning against confusing activity with effectiveness.
More spending can create more protection.
But only when spending is directed toward the risks that matter.
✅ Global Cybersecurity Spending Is Rising
The
✅ SMBs Face a Structural Security Disadvantage
The argument that smaller organizations can struggle with dedicated security personnel, advanced tooling and 24/7 monitoring is credible. Their limited resources can make them attractive targets and create downstream supply-chain risks.
⚠️ AI Does Not Automatically Reduce Security Costs
The claim is directionally sound, but
⚠️ More Security Tools Do Not Necessarily Mean Better Protection
Tool sprawl can produce duplication, alert fatigue and operational overhead. However, the exact number of tools an organization should operate depends heavily on its size, architecture and risk profile.
Prediction
(+1) Cybersecurity Will Become More Risk-Driven
Security programs will increasingly move away from counting vulnerabilities and toward measuring exploitable, business-relevant exposures.
(+1) Security Tool Consolidation Will Accelerate
Organizations under budget pressure are likely to prefer platforms that combine multiple defensive capabilities instead of maintaining dozens of disconnected products.
(+1) AI Will Become More Selective
Rather than applying expensive AI to every alert, mature organizations will reserve advanced models for investigations and tasks where they deliver measurable value.
(+1) Managed Security Will Grow Among SMBs
Small businesses are likely to rely increasingly on managed security providers because building a sophisticated internal security operation is financially unrealistic for many of them.
(+1) Cybersecurity FinOps Will Become a Real Discipline
Security leaders will increasingly track licensing, infrastructure, AI usage and engineering time alongside risk reduction.
(-1) Uncontrolled Tool Sprawl Will Become Harder to Justify
Organizations will increasingly retire overlapping products when they cannot demonstrate meaningful security improvements.
(-1) “More AI” Will Stop Being a Sufficient Security Strategy
Enterprises will become more skeptical of AI-powered security products that cannot demonstrate measurable improvements over existing workflows.
(+1) The Biggest Competitive Advantage Will Be Efficiency
The next generation of cybersecurity leaders may not be the companies selling the largest number of security products. They may be the companies capable of delivering the largest measurable reduction in risk with the smallest operational and financial burden.
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