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Introduction: A Better Score Does Not Always Mean Better Security
Cybersecurity teams love a rising number. A prevention score moving from 62% to 69% looks like progress, especially after a difficult year in which organizations struggled to keep pace with increasingly evasive attacks.
But there is a dangerous question hiding behind that improvement: what exactly did the defenses prevent?
That question sits at the center of the Blue Report 2026 research attributed to Picus Labs. The report examines enterprise prevention and detection effectiveness using attack simulations conducted in real customer environments. Its central message is uncomfortable but important: a security control can become extremely good at stopping an attack it recognizes while remaining surprisingly weak against another version of the same underlying behavior.
In other words, recognition is not the same thing as prevention.
Picus’s previous Blue Report research already showed why this distinction matters. Its 2025 report, based on more than 160 million attack simulations, reported that average prevention effectiveness had fallen from 69% in 2024 to 62% in 2025. It also found major weaknesses around credential abuse, data exfiltration, logging, and alert generation.
The 2026 findings described in the source article therefore represent an apparent recovery—but also reveal a deeper problem. The headline number may be improving while important portions of the attack surface remain dangerously exposed.
The Real Problem: Security Controls Often Recognize Procedures, Not Objectives
Traditional security validation frequently asks a relatively simple question: Did the security product recognize this attack?
That is useful, but it can create a false sense of confidence.
An attacker does not necessarily care whether they successfully execute one particular tool, command, malware sample, or procedure. They care about the outcome.
If the objective is credential theft, for example, there can be multiple technical paths toward the same result. Blocking one path does not automatically mean that the objective is protected.
This distinction becomes especially important as attackers increasingly modify malware, change tooling, abuse legitimate utilities, and blend malicious activity into normal administrative operations.
The file changes.
The hash changes.
The command changes.
The tool changes.
The behavior can remain almost identical.
IOC-Based Testing: Valuable, but Only at One Layer
IOC-based testing remains essential for security teams.
A firewall, secure email gateway, web proxy, or other perimeter control should absolutely be tested against known malicious indicators and known-bad samples.
If a malicious file is circulating and the security gateway recognizes it, blocking that sample is exactly what the control is supposed to do.
The problem appears when organizations interpret that success as proof that the underlying behavior is protected.
A signature can identify a specific artifact.
It cannot automatically understand every future implementation of the same attacker objective.
This is why IOC-based validation should be treated as one layer of assurance rather than the final answer.
TTP-Based Testing Changes the Question
Behavioral or TTP-based validation asks a much more difficult question.
Instead of asking whether a particular credential-stealing tool was detected, defenders should ask whether credential material can be accessed through any realistic route.
Instead of asking whether one lateral-movement utility was blocked, they should ask whether an attacker with legitimate credentials can move between systems.
Instead of asking whether one malware hash is detected, they should ask whether the security architecture recognizes the behavior that the malware is attempting to perform.
This is the difference between testing the procedure and testing the objective.
And in modern enterprise environments, the second question is increasingly the one that matters.
The Mimikatz Example Reveals the Blind Spot
The most striking example in the supplied Blue Report 2026 article concerns Mimikatz and credential-access behavior.
According to the article, the same tool was tested against the same broad objective using different credential-access paths.
The reported prevention results were dramatically different.
The classic LSASS memory-dumping path achieved a reported 94% prevention rate.
A different memory-based credential-access approach reportedly fell to 17%.
Reading LSA Secrets from the local registry reportedly produced an astonishingly low 3% prevention rate.
The significance is not really Mimikatz itself.
The significance is that all three approaches pursue the same general objective: OS Credential Dumping, mapped to MITRE ATT&CK technique T1003.
One path was highly recognizable.
Another was less recognizable.
The third blended more naturally into privileged system activity.
The security stack therefore appeared strongest when the attack looked exactly like something the controls had been trained or configured to recognize.
Why 94% Can Be More Dangerous Than It Looks
A 94% score sounds excellent.
But a security team should immediately ask another question: 94% against what exact implementation?
If the test uses a known build of a recognizable tool, a signature-based control may benefit from knowing exactly what the tool looks like.
Change the artifact and the control may lose confidence.
Recompile the software.
Alter recognizable strings.
Change the delivery mechanism.
Use another legitimate administrative component.
Move execution into a different process context.
The attacker may still be pursuing exactly the same objective.
This is why behavioral validation is so important. The purpose is not to make signatures irrelevant. Signatures are useful. The purpose is to ensure that a signature does not become a substitute for behavioral coverage.
The “Inside the Network” Problem Is Even Worse
The supplied article makes another important distinction between perimeter prevention and post-compromise behavior.
According to its reported findings, the overall prevention figure reached 69%, while simulated post-compromise actions performed by an authenticated user were reportedly blocked only 37% of the time.
That gap should make security leaders uncomfortable.
A perimeter score can tell you how frequently attacks are stopped before they cross the boundary.
It does not necessarily tell you what happens after an attacker obtains valid access.
Modern intrusions frequently depend on exactly that scenario.
An attacker does not always need to smash through the front door.
They may obtain credentials through phishing, malware, credential theft, social engineering, exposed secrets, or another compromised system.
Once valid access exists, the nature of the defensive problem changes dramatically.
Loud Attacks Are Easier to See
The article reports stronger results for behaviors such as lateral movement, UAC bypass, credential reuse, and Active Directory abuse.
That makes intuitive sense.
Some attacker behaviors generate distinctive telemetry.
Some create unusual process relationships.
Some produce authentication events.
Some interact with security-sensitive resources in ways that modern EDR and SIEM platforms are specifically designed to identify.
These are the attacks defenders have spent years learning to recognize.
But quiet discovery and passive collection can be much harder.
Quiet Collection Is the Dangerous Middle Ground
The reported figures for passive credential access, discovery, and collection are considerably weaker.
The article cites approximately 22% prevention for credential material accessed through memory and registry-oriented techniques, while discovery and collection reportedly reached only about 10% prevention.
This is where the security equation becomes uncomfortable.
An attacker does not necessarily need to trigger a spectacular security event.
They can spend time learning.
Which machines exist?
Which users have elevated privileges?
Which accounts are active?
Which shares contain valuable files?
Where are sensitive documents stored?
Which systems communicate with one another?
Which credentials can potentially unlock additional access?
Discovery is often the quiet preparation phase before a much louder attack.
The SharpHound Lesson: Visibility Must Include Reconnaissance
The article specifically highlights domain enumeration and local file collection as areas where defensive coverage can be weak.
This is particularly relevant to enterprise environments because attackers increasingly treat reconnaissance as a prerequisite rather than an afterthought.
An adversary who understands the environment can choose safer routes.
They can avoid noisy systems.
They can prioritize valuable identities.
They can identify trust relationships.
They can search for paths to privilege.
That means detection engineering cannot focus exclusively on ransomware execution or data destruction.
The activity that makes the final attack possible may happen much earlier.
Signature Security Is Not Dead—Its Role Has Changed
It would be a mistake to interpret these findings as an argument against signatures.
Signatures remain valuable.
Known malware should be blocked.
Known malicious infrastructure should be blocked.
Known exploit patterns should be detected.
Known indicators should be incorporated into defensive systems.
The mistake is treating those capabilities as comprehensive evidence of security.
The modern model should be layered:
Known bad should be blocked at the edge.
Known behavior should be detected inside the environment.
Unknown variations should be challenged through behavioral validation.
That is a far more resilient approach.
The Security Stack Does Not Necessarily Need to Become Bigger
One of the strongest ideas in the article is that organizations may not need another security product.
They may need better evidence about the products they already own.
A company can have EDR, SIEM, NDR, firewall, email security, identity protection, vulnerability management, cloud security, and endpoint controls—and still have major gaps.
The question is not how many tools are deployed.
The question is whether those tools work together against the attack behaviors that actually threaten the organization.
A badly tuned ten-product stack can be weaker than a carefully validated five-product stack.
Continuous Validation Is the Missing Feedback Loop
Security controls are not static.
Networks change.
Applications change.
Cloud infrastructure changes.
Identity permissions change.
EDR policies change.
Detection rules change.
Operating systems change.
Attackers change.
A control that successfully blocked an attack six months ago may not provide the same protection today.
That makes continuous validation increasingly important.
Picus’s current platform messaging also emphasizes continuous security validation, automated penetration testing, adversarial exposure validation, and evidence-based remediation.
The underlying philosophy is straightforward: do not assume that a security control works because it is deployed—prove that it works.
Deep Analysis: Turning the Findings Into Defensive Validation
Start With Your Detection Pipeline
Security teams should begin by identifying which attack behaviors are expected to generate telemetry.
For Windows environments, defenders can inspect relevant event channels and verify that security telemetry is actually reaching the SIEM.
For example:
Get-WinEvent -ListLog Security | Select-Object LogName, IsEnabled, RecordCount
This is not an attack command. It is a simple defensive check that helps establish whether expected Windows security logging is enabled and producing records.
Examine Recent Security Events
A basic event review can help determine whether the environment is generating the evidence analysts expect.
Get-WinEvent -LogName Security -MaxEvents 100 | Select-Object TimeCreated, Id, ProviderName, LevelDisplayName
The important question is not simply whether events exist.
The important question is whether the events necessary to investigate credential abuse, privilege changes, authentication anomalies, and lateral movement are present.
Verify PowerShell Logging
Because PowerShell is widely used by both administrators and attackers, defenders should validate that appropriate PowerShell telemetry is enabled.
Get-WinEvent -ListLog PowerShell | Select-Object LogName, IsEnabled, RecordCount
Security teams should then verify that the resulting telemetry reaches their central detection platform.
Test Detection Without Performing Credential Theft
A mature validation program should separate testing detection logic from actually extracting credentials.
Defenders can use approved simulation frameworks and harmless atomic tests to generate controlled behaviors without collecting real secrets.
The objective is to answer questions such as:
Does the endpoint generate telemetry?
Does the SIEM receive it?
Does the detection rule trigger?
How quickly does the alert appear?
Does the alert contain enough context?
Can analysts distinguish the activity from legitimate administration?
Validate the Entire Chain
The most important validation question is not:
Did EDR detect the tool?
It is:
Did the security stack detect the behavior?
That distinction should become part of every security validation exercise.
Measure Prevention and Detection Separately
A security control can prevent an action without generating a useful alert.
Another control can detect an action after it has already succeeded.
Therefore, prevention and detection should never be treated as interchangeable metrics.
Teams should record:
Prevention
Detection
Logging
Alert generation
Alert fidelity
Response time
Containment capability
This creates a much more realistic picture of defensive readiness.
Why the Edge Still Matters
The declining IOC-based prevention rate reported in the supplied article is also significant.
The article states that malware-download prevention fell from 60% to 50%, compared with 71% in 2024.
That means even the layer that should be particularly effective against recognizable malicious artifacts may be losing ground.
If confirmed by the full 2026 report, this would suggest that adversaries are increasingly succeeding in making malicious payloads look less recognizable or are finding delivery routes that bypass traditional edge controls.
The lesson is not to abandon perimeter defenses.
It is to stop treating them as the final line of defense.
The Rise of Legitimate Tools Makes Behavioral Detection Essential
Modern attackers increasingly abuse legitimate software and native operating-system functionality.
That creates a fundamental detection problem.
Blocking every legitimate administrative utility is impossible.
Blocking every scripting engine is impractical.
Blocking every signed executable would break the enterprise.
The defender therefore has to understand context and behavior.
Who launched the process?
From where?
Under which account?
At what time?
What parent process created it?
What resources did it access?
What happened immediately afterward?
Did the activity match normal administrative behavior?
This is where modern EDR and SIEM engineering becomes much more important than simple malware matching.
Detection Quality Matters More Than Alert Quantity
The supplied article also points toward a broader detection-engineering problem.
Its reported figures include a 58% logging score and only a 14% alert rate.
Those numbers, if confirmed in the complete Blue Report 2026 dataset, would represent a serious disconnect between visibility and actionable detection.
An organization may collect enormous quantities of telemetry and still fail to produce useful alerts.
Logging creates evidence.
Detection creates understanding.
Response creates action.
These are different stages.
A 58% Log Score Does Not Equal 58% Security
Logging is necessary, but it is not the same as security.
A security event sitting silently inside a SIEM is not necessarily protecting anyone.
Analysts need the right telemetry.
They need detection rules.
They need correlation.
They need context.
They need prioritization.
And they need a response process capable of acting on the result.
This is why detection engineering deserves the same validation discipline as prevention engineering.
The Four Decisions That Matter After Validation
The most useful outcome of security validation is not another dashboard.
It is a decision.
The organization should be able to determine whether an exposure should be:
Patch.
Mitigate.
Monitor.
Accept with evidence.
That last phrase is particularly important.
Risk acceptance should not mean pretending a weakness does not exist.
It should mean the organization understands the exposure, understands the compensating controls, documents the residual risk, and can explain why the decision is reasonable.
Why Automated Testing Is Becoming Necessary
Manual penetration testing remains valuable, but it cannot realistically reproduce thousands of behavioral variations continuously across a large enterprise.
This is where automated security validation becomes increasingly attractive.
Picus describes its Swarm technology as an orchestration layer designed to coordinate behavioral variations and validate them against deployed controls. The company has also increasingly positioned autonomous validation as a way to continuously connect exposure discovery, attack simulation, remediation, and re-validation.
The broader industry lesson extends beyond any single vendor.
Automation allows defenders to repeatedly ask the same uncomfortable question:
Does the control still work today?
What This Means for CISOs
A CISO should be suspicious of any security metric that does not explain what was actually tested.
A 90% score is meaningless without knowing the attack set.
A 70% prevention rate may be excellent for one category and disastrous for another.
A 100% detection rate against known malware says little about unknown behavioral variants.
Security metrics need context.
Otherwise, organizations risk optimizing the number rather than the defense.
What This Means for Security Engineers
For security engineers, the practical lesson is even clearer.
Do not validate only the attack everyone knows.
Validate the objective.
If the concern is credential theft, test multiple credential-access behaviors.
If the concern is lateral movement, test multiple legitimate and malicious movement paths.
If the concern is discovery, validate whether enumeration generates useful telemetry.
If the concern is exfiltration, test whether sensitive-data movement can be detected and controlled.
The attacker does not promise to use the technique from your last test.
What This Means for SOC Analysts
SOC teams should also challenge their assumptions.
When a detection fires, ask what behavior actually caused it.
When a detection does not fire, ask whether the underlying behavior was ever tested.
When an alert disappears after a tool change, ask whether the detection depended too heavily on a specific artifact.
The goal is to build detections that survive reasonable variations in attacker implementation.
What This Means for the Board
Executives should also change the question they ask.
Instead of:
How many security products do we have?
Ask:
“What attacks have we proven those controls can stop?”
Instead of:
What is our prevention score?
Ask:
Where does that score collapse?
Instead of:
Are we compliant?
Ask:
“What evidence demonstrates that our most important attack paths are actually defended?”
Those questions produce much better conversations.
What Undercode Say: The 40-Line Reality Check
- A Better Number Can Hide a Worse Problem
The reported move from 62% to 69% sounds encouraging.
But an average cannot describe every attack path.
2. Attackers Do Not Respect Security Categories
An attacker thinks in objectives.
Defenders often think in products.
That mismatch creates gaps.
- The Mimikatz Example Is More Important Than the Tool
The real lesson is not “Mimikatz is dangerous.”
It is that different implementations of the same objective can produce radically different defensive outcomes.
- Recognition Is Becoming a Weak Security Metric
If a control succeeds only when the attack looks familiar, it is vulnerable to variation.
5. Behavioral Validation Is the Necessary Counterweight
Defenders need to test what an attacker can accomplish, not merely what a known tool looks like.
6. Perimeter Security Still Matters
The edge remains critical.
But it cannot be the entire defensive strategy.
7. Assume Breach Has Become Operationally Relevant
Once valid credentials exist inside an environment, the security problem changes.
- Identity Is Now a Major Security Boundary
A legitimate account can be more useful to an attacker than a malicious executable.
9. Quiet Activity Deserves More Attention
Discovery and collection may not look dramatic.
That does not make them harmless.
- Telemetry Is Only Valuable If Someone Can Use It
A log that nobody receives or understands does not provide meaningful protection.
11. Detection Rules Must Be Tested
Writing a detection rule is not proof that the rule works.
12. SIEM Content Needs Adversarial Validation
If the SOC never challenges its own rules, blind spots can survive for years.
13. EDR Is Powerful but Not Omnipotent
Even excellent endpoint technology can miss behavior that falls outside its strongest detection patterns.
14. Security Products Are Not Magic
Deployment is the beginning of security engineering, not the end.
15. Attack Simulation Should Become Continuous
Annual testing is valuable.
Continuous validation is stronger.
- Variants Should Be Part of Every Serious Test
One procedure should never represent an entire technique.
17. Legitimate Tools Complicate Detection
Attackers can hide inside normal administrative activity.
18. Context Is the New Battleground
The same command can be legitimate in one environment and suspicious in another.
19. Security Teams Need Better Baselines
Without understanding normal behavior, unusual behavior becomes harder to identify.
20. Credential Theft Deserves Special Attention
Credentials can unlock multiple systems and extend an intrusion dramatically.
21. Active Directory Remains a High-Value Target
Identity infrastructure can turn one compromised endpoint into an enterprise-wide problem.
22. Discovery Is an Early Warning Signal
An attacker mapping the environment may already be preparing the next phase.
23. Prevention and Detection Are Different Jobs
Stopping an attack and recognizing a successful attack require different capabilities.
24. Both Need Independent Measurement
A strong prevention score does not excuse weak detection.
- A Strong Detection Score Does Not Excuse Weak Prevention
The attacker should not be allowed to succeed simply because the SOC will eventually see it.
26. Risk Decisions Need Evidence
Security leaders should know exactly what their controls can and cannot prove.
- More Products Are Not Always the Answer
Better configuration and validation can produce more value than another procurement cycle.
28. Security Budgets Should Follow Proven Gaps
Testing reveals where investment is actually needed.
29. Automation Will Become Increasingly Important
The number of possible attack variations is too large for purely manual validation.
30. AI Makes This Problem More Urgent
As attackers automate reconnaissance and adaptation, defenders need faster validation cycles.
31. Continuous Testing Creates Feedback
Attack simulation reveals a weakness.
Remediation addresses it.
Re-testing proves whether the fix worked.
- That Loop Is More Valuable Than a Static Score
A score tells you where you are.
A validation loop helps you improve.
33. Security Teams Should Test Their Assumptions
Every “protected” classification should have evidence behind it.
- The Best Question Is “What Happens Next?”
When an attacker gets initial access, what prevents them from reaching the next objective?
- Security Architecture Should Be Measured as a Chain
One strong control cannot compensate for a critical downstream failure.
- The Weakest Behavioral Path Can Define the Real Exposure
Attackers only need one viable route.
Defenders need enough coverage to close meaningful routes.
- The 69% Number Should Trigger Investigation, Not Celebration
The average is useful.
The exceptions are where the real risk lives.
38. Security Leaders Should Demand Attack-Path Evidence
Protected should mean demonstrated.
39. The Future of Defense Is Evidence-Driven
Assumptions are becoming increasingly expensive.
40. The Final Lesson Is Simple
Do not test whether your security stack recognizes the attack. Test whether the attacker can accomplish the objective.
✅ The 2025 Baseline Is Independently Supported
Picus publicly reports that its 2025 Blue Report was based on more than 160 million attack simulations and that overall prevention effectiveness declined from 69% in 2024 to 62% in 2025.
⚠️ The 2026 Report Claims Require Context
The specific 2026 figures in the supplied article—including 338 million simulations, the 69% prevention score, the 37% post-compromise figure, and the detailed Mimikatz percentages—could not be independently confirmed from the publicly indexed Picus Blue Report page available at the time of checking. Picus’s public Labs page currently prominently surfaces the 2025 Blue Report rather than a complete 2026 Blue Report dataset.
✅ Picus’s Broader Security-Validation Strategy Is Confirmed
Picus publicly describes breach and attack simulation, automated penetration testing, adversarial exposure validation, and continuous security-control validation as core elements of its platform. Its 2026 materials also emphasize autonomous validation and evidence-based remediation.
⚠️ Vendor-Produced Research Should Be Read as Vendor Research
The Blue Report is research produced by Picus Labs, meaning its methodology, dataset, scoring definitions, and interpretation should be evaluated directly rather than treated as a universal measurement of every organization’s cybersecurity posture.
Prediction
(+1) Behavioral Security Validation Will Become a Standard Enterprise Practice
The direction of cybersecurity strongly favors continuous validation rather than periodic assumptions.
As attackers increasingly modify tools, abuse legitimate software, automate reconnaissance, and exploit valid credentials, security teams will have greater difficulty relying exclusively on signatures and static indicators.
Organizations will increasingly measure whether security controls stop behaviors and objectives, not simply whether they recognize known malware.
(+1) Security Teams Will Test Attack Variations More Frequently
Future validation programs are likely to move away from one-test-per-technique models.
Teams will increasingly test multiple paths toward the same objective, particularly around credential access, privilege escalation, discovery, lateral movement, and data theft.
(+1) Continuous Validation Will Become More Closely Integrated With CTEM
Continuous Threat Exposure Management is fundamentally about identifying meaningful exposure, validating its real-world significance, prioritizing it, and reducing it.
That makes behavioral security validation a natural component of the broader CTEM workflow.
(-1) Static Prevention Scores Will Become Less Trustworthy on Their Own
A single organization-wide prevention percentage will increasingly fail to describe the complexity of modern attack paths.
As attacker variation increases, averages will hide more important exceptions.
(+1) AI Will Accelerate the Validation Cycle
AI-powered security testing is likely to make it easier to generate, execute, compare, and re-test large numbers of attack variations.
That does not eliminate the need for human security expertise.
It increases the amount of evidence that security teams can process.
(-1) Organizations That Validate Only Famous Attack Tools Will Continue to Carry Hidden Risk
If defenders continue testing only the most recognizable implementation of a technique, attackers will have opportunities to change the implementation while preserving the objective.
The most resilient organizations will therefore move from “Can we detect this tool?” toward “Can we detect and prevent this behavior regardless of the tool?”
Final Verdict: The Number Is Not the Security
The most important message from the supplied Blue Report 2026 article is not that prevention improved to 69%.
It is that a prevention score can tell you what a control recognizes without necessarily telling you what that control stops.
That distinction could become one of the defining cybersecurity lessons of 2026.
Security teams have spent years building enormous defensive stacks. Firewalls became next-generation firewalls. Antivirus became EDR. SIEM evolved toward XDR. Identity systems gained behavioral analytics. Cloud environments gained their own security layers.
Yet attackers still look for the gap between what the security system expects and what the attacker actually does.
That gap is where behavioral validation becomes essential.
A recognizable attack should be blocked.
An unfamiliar implementation of the same behavior should still be investigated.
A stolen credential should not become a free pass.
Discovery should generate meaningful visibility.
Lateral movement should encounter resistance.
Sensitive data should not be quietly collected without detection.
And every important security assumption should eventually face the same test:
Can we prove it?
That is the difference between owning security controls and knowing that they actually work.
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References:
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