The First Principle of Cybersecurity Has Not Changed — But the Cost Law Has Been Rewritten by AI

When Claude Code Security was announced on February 20, 2026, global cybersecurity stocks dropped almost immediately.

Not gradually.
Immediately.

The market reaction was not about feature comparison.
It was about structural economics.

Investors understood something fundamental:

Cybersecurity's first principle has not changed.
But the cost law governing the entire industry just did.

1. The First Principle: Cost-Constrained Conflict

Cybersecurity is not primarily a technology problem.

It is an economic equilibrium problem.

An attack occurs if and only if:

V - C_{A} > 0

Where:

V = value of the target
C_A = cost of executing the attack

As long as value exceeds cost, attack is rational.

Defense does not eliminate attackers.
Defense exists to modify the inequality:

C_{A}^{effective} > V

If attack cost exceeds value, rational attackers disengage.

This principle has always defined cybersecurity.
It still does.

2. Why Enterprises Don't Spend Equal to Maximum Loss

Let:

L = total loss if compromised (downtime + regulatory penalties + reputational damage + long-term impact)
P = probability of successful compromise

Historically, rational security spending follows:

C_{D} \approx P \cdot L

Organizations budget for expected loss, not total exposure.

This probabilistic framework defined the size of the cybersecurity industry for decades.

3. The OG Cost Law

At OpenGuardrails, we formalize the equilibrium as:

Θ_{OG} = \frac{C _{A}}{V}

If Θ_OG > 1: equilibrium is stable
If Θ_OG < 1: attack is economically rational

Cybersecurity exists to maintain:

Θ_{OG} > 1

This is the OG Cost Law.

For decades, the system hovered near balance because human-driven attack costs were meaningfully high.

4. What Claude Code Security Signals

Claude Code Security demonstrates a new capability:

Full codebase semantic reasoning
Discovery of subtle, context-dependent vulnerabilities
Automated patch suggestion
Scalable vulnerability verification

This is not incremental rule-based scanning.

It is machine-speed reasoning over software systems.

The same capabilities that help defenders reduce backlog also reduce attacker cost.

And this is the economic inflection point.

Before AI-assisted exploitation:

C_{A}^{human} = High

After AI-assisted discovery:

C_{A}^{AI} \to ϵ (ϵ \to 0)

The first principle remains unchanged.

But the denominator in the cost law collapses.

5. The Expansion of Attackable Targets

Previously, only targets satisfying:

V > C_{A}^{human}

were economically worth attacking.

Now:

V > ϵ

Which effectively means:

Every organization with non-zero digital value is attackable.

The attack surface expands from high-value enterprises to the entire digital economy.

6. The Collapse of the Probabilistic Budget Model

Historically:

P = 1 - (1 - p)^{m}

Where:

m = number of vulnerabilities
p = exploit probability

AI increases:

Vulnerability discovery rate
Exploit construction efficiency
Iteration speed

As discovery approaches completeness:

P_{AI} \to 1

Compromise becomes a time function rather than a probability.

When P → 1:

C_{D} \approx P \cdot L \to L

Security budgets structurally migrate toward full exposure.

This is why total cybersecurity spending will increase dramatically.

7. Why Defensive Costs Rise Faster Than Attack Costs

Attackers need to find one vulnerability.

Defenders must remediate all vulnerabilities.

Let:

N = number of targets
m = vulnerabilities per target

Defensive workload:

W = N \times m

AI expands both:

More targets become worth attacking
More vulnerabilities become discoverable

W_{AI} = N_{AI} \times m_{AI}

Attack cost trends toward zero.
Defensive workload expands multiplicatively.

To restore equilibrium:

C_{A}^{effective} > V

Defensive investment must increase.

Not slightly. Structurally.

8. Why the Market Reacted Immediately

The market understood:

This is not a feature competition.
It is a cost-structure reset.

Traditional cybersecurity vendors are built around:

Signature detection
Pattern matching
Historical threat databases
Human-limited vulnerability discovery

AI-native offense and defense operate on:

Semantic system reasoning
Context-aware exploit construction
Automated patch synthesis
Continuous machine-speed iteration

Historical rule libraries are not durable moats in this regime.

When the cost law changes, competitive advantages tied to the old cost structure evaporate.

9. The Structural Outcome

Two consequences follow directly:

1️⃣ Total cybersecurity revenue will rise significantly

Because attack probability increases and attackable targets expand.

2️⃣ Traditional vendors will be displaced

Because their architectures are optimized for a pre-AI equilibrium.

The industry does not shrink.
It resets.

The first principle did not change:

V - C_{A} > 0

But AI collapses C_A.

To maintain stability, cybersecurity must become:

AI-native
Continuous
Autonomous
Economically adaptive

Cybersecurity is no longer a tooling category.

It becomes an economic stabilization layer for the digital economy.

—

The market did not overreact.

It reacted to a rewritten cost law.

And when the cost law changes,
the industry changes with it.