The Consolidation Trap

For more than a decade, cybersecurity has been moving toward consolidation.

Organizations accumulated endpoint protection, vulnerability management, cloud security, identity security, SIEM, SOAR, container security, workload protection, threat intelligence, and dozens of specialized products.

The industry's response has been to consolidate. Bring more capabilities into fewer platforms. Create unified telemetry. Centralize policy. Reduce vendors. Reduce operational complexity.

There is a strong case for this approach. Gartner explicitly identifies lower total cost of ownership, improved efficiency, better integration, and broader controls coverage as reasons organizations pursue cybersecurity platform consolidation.

What if the capabilities being consolidated into the platform increasingly become capabilities that AI itself can perform?

If AI can investigate an alert, correlate telemetry, search vulnerabilities, write detection rules, analyze configurations, perform threat hunting, investigate identities, recommend remediation, and eventually execute many of those actions autonomously, organizations may eventually question what exactly they are paying a security platform to do.

The question changes from: How many security products can we consolidate?

to: How much security functionality should we buy when intelligence itself is becoming software?

And underneath that is an even more important question: What should remain a specialized security product when increasingly capable AI can perform the function?

The First Consolidation Wave

The original consolidation thesis was largely about reducing fragmentation. A simplified enterprise might have looked like:

Each system generated its own telemetry. Each had its own policies. Each required its own expertise. Each created another integration.

The platform model promised:

The value proposition was straightforward: Buy fewer products. Operate fewer products. Integrate fewer products.

That logic remains valid. But AI changes what happens inside the platform.

AI is Eating the Security Toolchain

AI can already perform parts of many tasks traditionally associated with security products and security operations.

And the direction of travel is toward increasingly autonomous execution.

The SANS 2026 SOC Report found that 79% of surveyed SOCs were already using AI or machine learning tools for security operations, although only 36% had incorporated AI into a defined SOC workflow.

Gartner's 2026 research goes further, describing AI agent-management platforms as a replacement for traditional automation approaches and arguing that security organizations need integrated control planes for multi-agent systems.

Microsoft is building toward an "agentic" security stack in which AI agents perceive, reason, and act across security operations, while its September 2026 announcement argues that separate protection and security-operations systems create handoffs and boundaries that slow machine-speed defense.

Palo Alto Networks is similarly deploying multiple AI models to continuously search customer environments for vulnerabilities and recommend fixes.

The Second Consolidation Wave

The first wave consolidated products. The second wave may consolidate intelligence.

Imagine an AI security system capable of:

Many of those capabilities currently exist as separate products, modules, analysts, workflows, or services. AI does not necessarily need a separate product for every one of them.

A sufficiently capable security reasoning system could potentially perform multiple functions through software. That creates an uncomfortable possibility for the cybersecurity industry: Some security products may eventually become interfaces around intelligence that customers could obtain elsewhere.

The Software Value Shift

This is not unique to cybersecurity. AI is already challenging the economics of software categories that historically sold specialized functionality.

If a general-purpose AI system can perform a task that previously required a dedicated application, the application does not automatically become worthless. But its value proposition changes.

The question becomes: What does the specialized product provide that the underlying intelligence does not?

Cybersecurity will face the same question. If an AI system can read a SIEM event, correlate related events, investigate the process tree, inspect cloud configuration, search threat intelligence, identify an attack path, generate a detection, recommend a policy, and execute a response... then simply packaging those functions into separate products becomes harder to justify.

The differentiator must move somewhere else.

AI Does Not Eliminate Security Products

This distinction is critical. The thesis is not: "AI will replace cybersecurity companies." That is too simplistic.

AI itself needs infrastructure. It needs telemetry. It needs permissions. It needs reliable data. It needs enforcement mechanisms. It needs identity. It needs trustworthy sensors. It needs secure execution.

And most importantly: AI can reason about an environment without necessarily controlling the underlying execution boundary.

• An AI security analyst can conclude: "This process should not access that credential." Something still has to enforce that decision.
• An AI agent can determine: "This workload appears compromised." Something still has to isolate the workload.
• An AI system can discover: "This process is attempting an unexpected network connection." Something still has to block it.

This distinction creates an important architectural boundary.

Intelligence is Becoming Commoditized. Control is Not.

This may become one of the most important shifts in cybersecurity. Consider two layers:

That is much harder to commoditize. The closer security moves to the execution boundary, the more valuable that enforcement layer becomes.

The New Cybersecurity Stack

This suggests a possible future architecture:

The upper layer becomes increasingly intelligent. The lower layer becomes increasingly important. And this is where the consolidation thesis starts to break.

The Platform May Consolidate Intelligence Without Consolidating Execution

An enterprise may eventually have one AI-driven security reasoning layer. That system could consume endpoint telemetry, cloud telemetry, identity data, network data, vulnerability information, runtime events, and application context. It could reason across all of them.

But the execution layer may still need to remain distributed.

A workload running on a Linux node cannot wait for an AI model in a distant control plane to reason about every syscall before the kernel allows it. A Kubernetes workload cannot depend on a remote AI investigation for every local security decision. A compromised process cannot be allowed to continue running indefinitely while a centralized platform finishes its analysis.

The AI Platform Paradox

Here is the paradox. The more capable AI becomes, the fewer specialized analytical functions organizations may need to purchase independently. But the more autonomous AI becomes, the more important the underlying execution controls become.

The value doesn't disappear. It moves down the stack.

The Cost of the AI Security Platform

There is another problem. AI itself is computationally expensive.

Google Cloud's 2026 research on AI infrastructure describes inference-related costs involving data movement, storage, specialized hardware utilization, and operational complexity. It also warns that organizations can burn significant compute resources on low-value workloads when scaling agentic security operations.

This creates a strange future possibility. An organization could consolidate ten security products into one AI-powered security platform. The platform could then deploy AI agents to inspect AI workloads using AI models running on expensive compute to determine whether the workload needs protection.

The security system itself becomes a substantial computational workload. That doesn't make AI security economically irrational. It means AI security itself must be optimized.

The Compute Tax of Intelligence

Suppose an organization has a security AI capable of investigating millions of events.

The question is no longer merely: How accurate is the AI?

It becomes: How much compute does the AI consume to produce useful security decisions? And: How much of that computation is actually necessary?

An AI system that analyzes every event at maximum depth may produce excellent intelligence. It may also produce an enormous bill.

This is why the security architecture of the future may need to separate continuous awareness from continuous deep reasoning.

Not Every Event Deserves an AI Investigation

Imagine a million runtime events. Most are routine. A small fraction are unusual. An even smaller fraction represent meaningful security risk.

Applying maximum AI reasoning to all million events is computationally wasteful. A more intelligent architecture could operate hierarchically:

This is another reason adaptive security matters. AI should not replace optimization. AI should make optimization more intelligent.

The Consolidation Trap

The trap is therefore not: Consolidation is bad.

The trap is: Assuming that consolidating every security capability into one platform remains the optimal architecture as AI absorbs more of those capabilities.

Imagine paying separately for AI-driven investigation, threat hunting, vulnerability analysis, detection engineering, incident summarization, attack-path analysis, and remediation.

If the underlying intelligence becomes increasingly general-purpose, the economic distinction between these products can weaken. The enterprise may eventually ask:

That is a legitimate strategic question.

The Platform Still Needs a Reason to Exist

This creates a much higher bar for security vendors.

A platform can no longer justify itself simply by saying: "We have more AI." Everyone will have AI.

It cannot simply say: "We correlate more data." AI systems will increasingly correlate enormous amounts of data.

It cannot simply say: "We automate investigation." Autonomous investigation will become increasingly commonplace.

The durable value needs to be closer to the execution boundary: Trusted telemetry. Identity. Context. Policy. Enforcement. Isolation. Runtime control. Infrastructure integration.

These are the mechanisms through which intelligence becomes action.

The Future May Be Less About More Security Products

Cybersecurity could therefore experience an unusual transition. The number of security capabilities available to an organization could increase dramatically. The number of products required to access those capabilities could decrease.

AI could act as an abstraction layer over many traditional security functions. That creates pressure on product categories whose primary value is analysis rather than control.

This doesn't mean those categories disappear. It means their differentiation becomes harder.

From Security Consolidation to Security Compression

A useful way to describe this transition is Security Compression.

Instead of 10 products → 10 specialized functions, we may move toward 1 intelligence layer → many security functions. AI compresses the software required to perform analytical work.

But there is a second compression problem: How much infrastructure does the security layer require to perform those functions? That creates two simultaneous optimization problems:

• FUNCTIONAL COMPRESSION: How many security workflows can intelligence perform?
• COMPUTATIONAL COMPRESSION: How much compute is required to perform them?

The future security architecture will need to solve both.

Where Opsonance Fits

This is the fundamental opportunity Opsonance is exploring.

Opsonance should not attempt to become another enormous platform containing every cybersecurity capability. Nor should it compete with increasingly capable AI systems on generic reasoning alone.

Instead, its opportunity is to occupy the layer between intelligence and execution. AI can reason. Opsonance can provide the runtime context and control needed to turn that reasoning into enforcement.

The architecture becomes:

The intelligence layer can evolve rapidly. The runtime enforcement layer remains anchored to the infrastructure.

And This Changes the Value Proposition

If AI increasingly commoditizes security reasoning, Opsonance should not sell: "We have an AI that analyzes security events." That is likely to become table stakes.

That architecture can coexist with enterprise AI, third-party security AI, SIEMs, CNAPPs, EDRs, internal security agents, and autonomous SOC systems. Opsonance does not necessarily need to replace all of them. It can provide the execution substrate beneath them.

The Cybersecurity Platform of the Future May Look Different

The future may not be one vendor with a monolith (EDR, SIEM, CNAPP, IAM, SOAR, AI Security, Vulnerability Management).

It may increasingly look like:

Intelligence becomes increasingly interchangeable. The control layer becomes increasingly important.

The New Security Economics

This also changes what customers should evaluate. Instead of simply asking "How many capabilities are included?", organizations may need to ask:

• How much intelligence is genuinely proprietary?
• How much could general-purpose AI perform?
• What happens if the AI becomes unavailable?
• Where are decisions enforced?
• How much compute does the security system consume?
• Can the enforcement layer operate independently?
• Can the organization replace the intelligence layer without replacing its security controls?

These questions create a different definition of platform value.

Consolidation is Still Useful

The answer is not fragmentation. Organizations do not want dozens of disconnected security systems. Unified context matters. Shared identity matters. Centralized policy matters. Integrated workflows matter. Gartner's research continues to support consolidation as a means of reducing complexity and improving efficiency.

But consolidation should not become an excuse for creating a monolithic security system that assumes every capability must remain proprietary and permanently bundled. The better architecture may be: Unified intelligence, open interfaces, distributed enforcement, adaptive resource allocation.

The AI Test

Every future security product may eventually face a simple question:

If the answer is: "Most of it is the AI," then the product is exposed to commoditization.

If the answer is: "The AI becomes better, but our runtime enforcement, telemetry, identity, infrastructure integration, and control capabilities become more valuable," then the product has a different kind of durability.

That is the distinction Opsonance is interested in.

From Consolidation to Optimization

The cybersecurity industry began by accumulating specialized products. Then it began consolidating them. AI introduces another transition. It can compress multiple analytical capabilities into increasingly general intelligence.

That means the next optimization may not be: How many security products can we put into one platform? It may be: Which security capabilities actually need to remain products at all?

And once that question is asked, another follows: Where should specialized security infrastructure remain because AI cannot replace the execution boundary?

The answer increasingly points toward the layers where software becomes action: Identity. Runtime. Kernel. Network. Workload. Enforcement.

This is where cybersecurity may retain durable infrastructure value even as intelligence becomes increasingly commoditized. The future may therefore not belong to the platform with the largest number of security features. It may belong to the architecture that knows which capabilities should be intelligent, which should be infrastructure, and which should disappear into AI.