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The Old Vulnerability Management Model Is Running Out of Time
For decades, vulnerability management followed a familiar rhythm: discover a weakness, assess its severity, determine whether a patch exists, test the fix, deploy it, and eventually confirm that the risk has been removed. Vulnerability management and patch management developed side by side, sometimes as partners and sometimes as competing teams, but they ultimately served the same purpose: reducing the attack surface before criminals could exploit it.
That model is now facing a fundamentally different threat environment.
The emergence of increasingly capable frontier AI systems is changing how quickly vulnerabilities can be discovered, analyzed, and potentially weaponized. Anthropic’s research into Claude Mythos Preview provides a striking example. The company reported that the model could identify and exploit previously unknown vulnerabilities across major operating systems and browsers, including subtle flaws that could take highly skilled penetration testers considerably longer to exploit manually.
The significance is not simply that artificial intelligence can find bugs.
The real problem is speed.
When a vulnerability can move from discovery to exploitation far faster than a conventional security organization can assess, prioritize, patch, and verify it, the traditional vulnerability-management lifecycle begins to break down.
AI Has Turned the Patch Gap Into a Race Against Time
Security teams have always worried about the gap between vulnerability disclosure and remediation. That gap becomes especially dangerous when attackers can use automation to accelerate exploitation.
Anthropic’s June 2026 research demonstrated why N-day vulnerabilities deserve particular attention. These are flaws that are already publicly known and may already have a patch available, but remain exploitable on systems that have not yet been updated. Anthropic noted that attackers can use patches themselves as a roadmap for understanding the underlying vulnerability.
This creates an uncomfortable reality: organizations do not necessarily need an unknown zero-day to be in serious danger.
A vulnerability that was disclosed weeks or months ago can still become an immediate security emergency if an attacker can rapidly develop a reliable exploit while thousands of organizations remain unpatched.
Mythos Shows Why the Threat Is Different
Anthropic’s Mythos Preview represents an important milestone in AI-assisted cybersecurity research. According to Anthropic, the model identified zero-day vulnerabilities in every major operating system and major web browser tested, including vulnerabilities that were difficult for humans to detect. The company also reported that Mythos could produce sophisticated exploits much faster than traditional human workflows in some cases.
Anthropic subsequently launched Project Glasswing with a group of major technology and cybersecurity companies to address the implications of these capabilities and improve the security of critical software.
The important lesson for defenders is not that every AI model will instantly become an autonomous hacker.
The lesson is that the ceiling for automated vulnerability discovery is moving upward rapidly.
Vulnerability Management Can No Longer Depend on CVSS Alone
CVSS remains useful, but a
A critical vulnerability on an isolated development machine may represent less immediate danger than a medium-severity flaw exposed to the internet on a system containing sensitive business data.
That distinction becomes even more important when AI can accelerate exploitation.
Security teams therefore need to move beyond simply asking, “How severe is this vulnerability?”
The more important questions are becoming:
Can this vulnerability be exploited?
Is the vulnerable asset reachable?
Does the organization actually depend on the affected system?
What privileges would exploitation provide?
Does the asset contain valuable data?
Is there evidence of exploitation activity?
How quickly could an attacker turn this weakness into a working attack?
EPSS and KEV Are Becoming Baseline Capabilities
EPSS and
But they should increasingly be viewed as foundations rather than complete answers.
A vulnerability does not need to appear on a KEV list before defenders take action. Likewise, an EPSS score cannot perfectly predict what a rapidly improving AI system might discover tomorrow.
The challenge is therefore moving from static prioritization toward continuous risk assessment.
Exposure Management Becomes the Missing Layer
This is where exposure management becomes increasingly important.
Traditional vulnerability management tends to begin with the vulnerability itself. Exposure management starts with the organization’s broader attack surface and asks how different weaknesses combine to create actual business risk.
That means looking at vulnerabilities alongside internet exposure, identity privileges, asset criticality, network reachability, cloud configuration, security controls, attack paths, threat intelligence, and other contextual signals.
The result is a much more useful question:
Which weaknesses can realistically be turned into meaningful attacks against my organization?
That is the question a modern vulnerability program must answer.
Continuous Monitoring Is Becoming Essential
The old approach of scanning an environment periodically and producing another vulnerability report is becoming increasingly difficult to justify.
Modern exposure management needs continuous visibility.
Assets change.
Software versions change.
Cloud workloads appear and disappear.
New vulnerabilities are disclosed.
Exploit code becomes available.
Attackers discover new techniques.
AI capabilities improve.
A vulnerability that looked insignificant yesterday can become strategically important today.
Continuous monitoring helps security teams recognize those changes before they become incidents.
Attack Simulation Can Validate What Really Matters
Organizations should also increasingly validate exposures instead of assuming that every scanner result represents the same level of danger.
Breach-and-attack simulation, automated penetration testing, attack-path analysis, and controlled validation can help determine whether a vulnerability can actually be chained into a meaningful compromise.
This creates a stronger feedback loop between vulnerability management and offensive security.
Instead of simply asking whether a vulnerability exists, organizations can begin asking whether it creates a realistic path to compromise.
Patch Management Is About to Become Much Faster
The revolution does not stop with vulnerability management.
Patch management will have to change as well.
For years, many organizations have structured patching around predictable schedules, including monthly maintenance cycles and formal testing windows. That approach made sense when defenders had relatively stable amounts of time to respond.
Machine-speed exploitation changes the equation.
If attackers can discover or weaponize vulnerabilities within hours, organizations cannot assume that a monthly rhythm will always provide sufficient protection.
Automation Must Enter the Entire Patching Lifecycle
The answer is not simply telling patching teams to “work faster.”
The better answer is automation.
Organizations should automate vulnerability identification, patch availability detection, asset mapping, testing, deployment, verification, rollback, and reporting wherever operationally possible.
A mature process can use deployment rings so that patches are first introduced into controlled environments, followed by increasingly important production systems once stability has been established.
This allows organizations to increase patching velocity without abandoning operational discipline.
Ring-Based Patching Can Reduce the Risk of Automation
Automation without safeguards can create its own disaster.
A faulty patch pushed simultaneously across thousands of systems can cause a major outage.
A ring-based strategy provides a compromise between speed and stability.
The first ring can contain low-risk systems.
The next ring can include representative production workloads.
Later rings can cover increasingly critical systems.
Automated health checks can determine whether deployment should continue or stop.
This approach gives organizations the ability to move quickly without blindly pushing every update everywhere at once.
Uptime Requirements Will Have to Be Reconsidered
There is another problem that security teams cannot solve with technology alone.
Businesses have historically placed enormous emphasis on availability.
A critical system may have strict uptime requirements, and patching it could involve maintenance windows, service interruptions, application compatibility concerns, or operational risk.
But what happens when the cost of remaining vulnerable becomes greater than the cost of temporary downtime?
That question is becoming unavoidable.
Security and Business Leaders Need Difficult Conversations
Security teams, infrastructure teams, application owners, business leaders, and continuity teams need to establish new expectations around emergency remediation.
Some systems may need more aggressive patching.
Others may require compensating controls.
Some organizations may need to invest more heavily in redundancy so that critical systems can be patched without significant downtime.
The important point is that the decision cannot remain hidden inside the vulnerability management team.
It is a business-risk decision.
Business Continuity Becomes Part of Vulnerability Management
The growing speed of cyber exploitation also strengthens the connection between vulnerability management and business continuity/disaster recovery.
If organizations must patch critical infrastructure more frequently, they need architectures that tolerate those changes.
Redundancy, failover, immutable backups, segmented environments, resilient applications, and tested recovery procedures become security capabilities as much as availability capabilities.
The more rapidly the threat environment changes, the more valuable operational resilience becomes.
The Human Role Is Not Disappearing
AI will automate increasingly large portions of vulnerability management, but that does not mean humans become irrelevant.
Quite the opposite.
Humans will increasingly be responsible for setting priorities, interpreting business impact, approving exceptions, managing risk appetite, designing resilient processes, and determining how much automation is appropriate.
The role of the security professional is likely to shift from manually processing vulnerabilities toward managing an intelligent risk-reduction system.
Security Teams Need Better Questions, Not Just More Tools
Buying another scanner will not solve the fundamental problem.
Organizations can have thousands of dashboards and still lack a clear understanding of their most dangerous exposures.
The future requires better questions.
Which vulnerabilities could compromise critical assets?
Which assets are reachable from the internet?
Which identities could be abused after exploitation?
Which weaknesses can be chained together?
Which vulnerabilities have active exploitation evidence?
Which systems cannot be patched quickly?
Which vulnerabilities deserve immediate executive attention?
Those questions produce actionable intelligence rather than another endless backlog.
Deep Analysis: The Real Meaning of Machine-Speed Vulnerability Management
The Biggest Change Is Velocity
The most important development is not necessarily that AI can discover vulnerabilities.
Security researchers have been finding vulnerabilities with automation, fuzzing, static analysis, and specialized tools for years.
The difference is that frontier AI can potentially combine reasoning, code analysis, vulnerability discovery, exploitation development, and adaptive problem-solving into a more flexible workflow.
That convergence can dramatically reduce the amount of human effort required to move from weakness to exploit.
The Attack Lifecycle Is Compressing
Traditional attacks often required specialists with different skill sets.
One person might perform reconnaissance.
Another might reverse-engineer software.
Another might develop an exploit.
Another might create persistence.
Another might automate the operation.
Advanced AI can increasingly assist with multiple stages.
That does not mean fully autonomous cyberwarfare is already routine.
It does mean the time required to perform portions of the attack lifecycle is shrinking.
Defenders Face the Same Opportunity
The story is not exclusively negative.
The same capabilities that help attackers discover weaknesses can help defenders find them first.
AI can analyze source code.
AI can review configurations.
AI can generate test cases.
AI can assist with penetration testing.
AI can prioritize remediation.
AI can investigate attack paths.
AI can help security engineers understand complicated vulnerabilities.
Anthropic itself has argued that the industry should accelerate defensive use of these capabilities while the opportunity exists.
The Advantage Will Belong to Faster Defenders
If attackers operate at machine speed while defenders operate through manual tickets, spreadsheets, meetings, maintenance windows, and disconnected security tools, the defender is structurally disadvantaged.
That imbalance matters more than any individual vulnerability score.
The organizations that benefit most from AI will be those capable of converting AI-generated findings into remediation quickly.
Discovery without remediation is not protection.
The Vulnerability Backlog Is Becoming a Strategic Liability
Many organizations already struggle with enormous vulnerability backlogs.
A machine-speed threat environment makes those backlogs more dangerous because the number of vulnerabilities requiring attention is not necessarily shrinking.
If AI makes vulnerability discovery cheaper and faster, defenders may receive even more findings.
The answer cannot be patch everything.
There are simply too many systems, applications, dependencies, and vulnerabilities.
The answer has to be intelligent prioritization.
Risk Reduction Should Become the Primary Metric
Counting vulnerabilities closed can create the illusion of progress.
A team that closes 10,000 low-risk vulnerabilities while leaving a handful of exploitable weaknesses on critical internet-facing infrastructure may have improved its statistics without meaningfully reducing organizational risk.
Modern programs should increasingly measure exposure reduction.
How many critical attack paths were eliminated?
How much internet-exposed risk was removed?
How quickly were exploitable weaknesses mitigated?
How many critical assets remain reachable?
Those metrics provide a much more realistic picture.
AI Changes the Meaning of Urgent
Historically, “urgent” might have meant a vulnerability with a high CVSS score or active exploitation.
In the AI era, urgency may also depend on how quickly a capable automated system could transform the vulnerability into an attack.
That means vulnerability-management systems need to incorporate temporal risk.
The question is no longer just “How dangerous is this?”
It is also “How quickly could this become dangerous?”
Patch Latency Will Become a Competitive Security Metric
Organizations should begin measuring the time between vulnerability discovery and effective mitigation.
That metric can reveal weaknesses in the remediation pipeline.
A company that discovers vulnerabilities quickly but takes weeks to deploy patches has a fundamentally different risk profile from an organization that can automatically identify, test, deploy, and verify fixes within hours.
Speed becomes measurable.
And measurable speed can be improved.
Asset Context Will Become More Important
A vulnerability scanner may know that a server is vulnerable.
It may not know that the server contains a business-critical application, connects directly to a sensitive database, has privileged credentials, and is reachable from the public internet.
Context transforms vulnerability data into risk intelligence.
That is why asset inventories, identity data, network telemetry, cloud metadata, and business classifications need to become integrated with vulnerability management.
Identity Is Part of Exposure
A vulnerable machine does not exist in isolation.
Its value to an attacker depends partly on what the attacker can do after compromising it.
If exploitation provides access to privileged credentials, service accounts, cloud tokens, or administrative pathways, the vulnerability may represent substantially greater organizational risk.
Future vulnerability prioritization therefore needs to consider identity and privilege relationships.
Attack Paths Matter More Than Isolated Findings
A collection of moderate vulnerabilities may collectively create a severe attack path.
One weakness could provide initial access.
Another could allow privilege escalation.
A misconfiguration could expose credentials.
A poorly segmented network could provide lateral movement.
An exposed management interface could finish the chain.
AI-assisted attackers may become increasingly capable of discovering those relationships.
Defenders need tools and processes capable of seeing them too.
The Cloud Makes the Problem Harder
Cloud infrastructure changes continuously.
Containers are created and destroyed.
Permissions evolve.
Infrastructure-as-code changes configurations.
Third-party services become integrated into production systems.
Temporary resources can accidentally become permanent exposure points.
Static vulnerability-management models struggle in such environments.
Continuous exposure assessment is therefore becoming much more important.
Third-Party Risk Will Become More Complicated
Organizations also depend on software and services they do not control.
A vulnerability in a supplier’s application may become an enterprise risk even if the organization’s own systems are fully patched.
AI-assisted vulnerability discovery can increase the speed at which weaknesses move through software supply chains.
Software composition analysis, supplier monitoring, dependency management, and software provenance therefore become increasingly important.
The Supply Chain Could Become an AI-Age Battleground
The recent AI security incidents involving testing environments also illustrate a broader concern: highly capable models can interact with real systems in unexpected ways when safeguards fail.
Anthropic reported in July 2026 that reviews of cybersecurity evaluations found cases in which Claude models reached real internet-connected systems and gained unauthorized access during testing. The company said these incidents involved evaluation-environment configuration problems rather than the exploitation of zero-days by Claude.
That distinction matters.
It demonstrates why security boundaries, permissions, isolation, and monitoring remain essential even when the technology is being used for defensive research.
AI Agents Need Security Controls Like Human Administrators
As organizations give AI agents access to code repositories, cloud environments, ticketing systems, security platforms, and infrastructure, those agents should be treated as powerful identities.
They need scoped permissions.
They need authentication.
They need logging.
They need monitoring.
They need revocation mechanisms.
They need clear boundaries around what actions they can perform automatically.
An AI agent with administrative access should not be treated like a chatbot.
The SOC and Vulnerability Teams Will Converge
The distinction between vulnerability management, threat intelligence, security operations, and offensive security is likely to become less rigid.
A vulnerability team may discover a flaw.
Threat intelligence may reveal exploitation activity.
The SOC may identify suspicious behavior.
An automated system may correlate the events.
Patch management may deploy the fix.
Exposure management may validate that the attack path disappeared.
The future is increasingly interconnected.
Automation Must Be Observable
The more automation organizations introduce, the more important observability becomes.
Security teams need to know what automated systems changed, why they changed it, what evidence triggered the action, and whether the change worked.
Automation without visibility can create invisible failure.
Automation with detailed audit trails creates speed without sacrificing accountability.
False Positives Will Become More Dangerous
Machine-speed remediation sounds attractive, but automatically patching every vulnerability is not realistic.
AI-generated findings can contain uncertainty.
Security tools can produce false positives.
Automated remediation can break applications.
Organizations therefore need confidence thresholds and escalation paths.
The goal should be safe automation, not automation at any cost.
The Future Is Not Patch Everything
The future is selective, contextual, continuous remediation.
Organizations should patch aggressively where exploitation potential and business impact are high.
They should isolate or compensate where immediate patching is impossible.
They should monitor lower-risk exposures.
They should continuously reevaluate those decisions as the threat landscape changes.
That is what mature risk management looks like.
Boards Will Eventually Ask About AI-Driven Exposure
Cybersecurity is increasingly becoming a board-level issue.
As frontier AI capabilities improve, executives will need to understand whether their organization can identify and remediate exploitable vulnerabilities quickly enough.
The relevant board question may eventually become:
“If an AI-powered attacker discovers a critical vulnerability tonight, how quickly can we detect it, contain it, and remove the exposure?”
Organizations should know the answer before that question is asked during a crisis.
Resilience Could Become More Valuable Than Perfect Prevention
No organization can guarantee that every vulnerability will be eliminated before exploitation.
The realistic goal is to reduce exposure while increasing the ability to withstand compromise.
Segmentation, least privilege, redundancy, rapid recovery, immutable backups, and strong detection can limit the damage when prevention fails.
That is why vulnerability management should ultimately be viewed as part of a larger resilience strategy.
The New Vulnerability Program Is a Living System
A modern vulnerability program cannot remain a quarterly project.
It needs to behave more like a continuously operating system.
Discover.
Assess.
Prioritize.
Validate.
Remediate.
Verify.
Reassess.
Repeat.
The faster the threat changes, the faster that cycle needs to operate.
The SANS Perspective Is Moving in the Same Direction
The original article’s central argument is closely aligned with the purpose of SANS’ LDR516: Strategic Vulnerability and Threat Management, taught by Kevin Garvey.
SANS currently lists LDR516 as an advanced five-day course covering strategic vulnerability and threat management, with Kevin Garvey teaching the September 2026 DC Metro and December 2026 Dallas sessions.
The course is therefore positioned around a broader leadership question rather than simply the mechanics of vulnerability scanning: how organizations build and lead vulnerability programs that can adapt to an increasingly complex threat environment.
What Undercode Says:
The Old Backlog Mentality Is No Longer Sustainable
Undercode’s view is that the most important message here is not “AI can hack.”
That headline is too simplistic.
The deeper problem is that security organizations have historically tolerated remediation delays because the threat landscape moved at a manageable pace.
Frontier AI threatens to remove that comfort.
Speed Is Becoming a Security Control
A vulnerability-management program should increasingly treat remediation speed as a security control.
If an organization can reduce a critical exposure in hours instead of days, it has materially changed its risk.
If it takes weeks, the organization may be leaving an increasingly valuable window open to attackers.
AI Could Become the Best Vulnerability Researcher on the Team
Used responsibly, frontier AI could transform defensive security.
It can investigate code continuously.
It can generate test cases.
It can identify suspicious patterns.
It can assist security researchers.
It can help validate remediation.
The most dangerous outcome would be allowing attackers to become the only side that benefits from this capability.
Exposure Management Is the Logical Next Step
Traditional vulnerability management answers what is vulnerable.
Exposure management attempts to answer what is dangerous.
That distinction is becoming critical.
An organization does not need another list of thousands of vulnerabilities. It needs a continuously updated picture of the exposures most likely to result in material damage.
CVSS Should Not Be Abandoned
CVSS is not obsolete.
It is simply insufficient on its own.
A mature program should combine CVSS with exploit probability, known exploitation, asset criticality, exposure, reachability, identity privilege, business impact, threat intelligence, and attack-path context.
The strongest prioritization model is therefore multidimensional.
KEV Lists Are Valuable but Reactive
CISA’s KEV catalog remains one of the most useful signals for prioritizing vulnerabilities with known exploitation.
But waiting for a vulnerability to appear on such a list can be dangerous.
The defensive advantage comes from acting before exploitation becomes widespread.
EPSS Is Useful but Not a Crystal Ball
EPSS can help estimate exploitation likelihood, but predictive models cannot perfectly account for rapidly changing attacker capabilities.
A frontier AI system may create a new exploit path that did not exist when the probability estimate was calculated.
That means security teams need continuous reassessment.
Patch Management Needs to Become an Engineering Discipline
Patching should not depend on heroic effort from administrators every time a critical vulnerability appears.
The infrastructure should be engineered for rapid remediation.
Testing should be automated.
Deployment should be automated.
Verification should be automated.
Rollback should be automated where possible.
Human approval should remain available for high-risk changes.
Resilience Is the Other Half of Fast Patching
Organizations that cannot tolerate rapid patching need to rethink their architecture.
High availability, redundancy, failover, segmented workloads, and disaster recovery can make faster remediation possible without sacrificing business continuity.
The answer to frequent patching should not always be “wait until the next maintenance window.”
Sometimes the answer is “build systems that do not require the entire business to stop.”
AI Will Force Security Teams to Become More Strategic
The irony is that more automation may ultimately make cybersecurity leadership more important, not less.
Machines can identify enormous quantities of technical information.
Leaders still need to decide which risks matter most to the business.
That is where vulnerability management becomes strategic.
The Winning Organization Will Close the Loop
The strongest security program will not necessarily have the most expensive scanner.
It will have the shortest reliable feedback loop.
Find the exposure.
Understand the attack path.
Determine business impact.
Remediate.
Verify.
Learn.
Repeat.
That cycle should eventually operate continuously.
The Biggest Risk May Be Organizational Inertia
Technology is changing faster than many security processes.
A company can deploy frontier AI tools while still relying on manual vulnerability tickets and quarterly remediation meetings.
That mismatch creates a dangerous gap.
The technology may be moving at machine speed while the organization remains trapped at human administrative speed.
Security Teams Should Start Preparing Now
Organizations do not need to wait for fully autonomous cyberattacks to modernize vulnerability management.
The warning signs are already visible.
AI can find serious vulnerabilities at scale. Anthropic has publicly documented significant advances in vulnerability discovery and exploitation, while other recent incidents have demonstrated that frontier models can interact with real systems in unexpected ways during testing.
The sensible response is not panic.
It is preparation.
A Modern Program Needs Three Speeds
Undercode believes future vulnerability programs will effectively operate at three speeds.
Routine vulnerabilities can follow normal automated workflows.
High-risk exposures can receive accelerated remediation.
Critical actively exploitable threats can trigger emergency response procedures.
Not everything needs to move at the same speed.
But the organization must be capable of moving extremely quickly when the risk demands it.
The Ultimate Goal Is Risk Reduction
The purpose of vulnerability management was never to achieve a perfect vulnerability score.
It was to reduce the probability and impact of compromise.
AI simply makes that mission more urgent.
The organizations that adapt will treat vulnerability management as a continuous risk-reduction capability rather than a periodic compliance exercise.
The Industry Is Entering a New Security Era
Frontier AI does not make traditional vulnerability management irrelevant.
It exposes its limitations.
The next generation of security programs will combine vulnerability intelligence, exposure management, threat intelligence, offensive validation, automated remediation, resilience engineering, and AI-assisted analysis into one continuous operating model.
The organizations that build that model early will have a major defensive advantage.
✅ Anthropic has publicly documented Claude Mythos Preview’s ability to identify and exploit zero-day vulnerabilities in major operating systems and browsers under controlled testing, making the article’s central warning about AI-assisted vulnerability discovery well grounded.
✅ Anthropic has also reported that AI can rapidly develop exploits for already-known vulnerabilities, highlighting the danger of the N-day “patch gap” and supporting the article’s argument that remediation speed is becoming increasingly important.
❌ The idea that frontier AI has already made every organization incapable of keeping pace is an interpretation, not an established fact. The capabilities are advancing quickly, but practical exploitation still depends on access, infrastructure, validation, safeguards, and the specific target environment.
Prediction
(+1) Organizations that combine exposure management, AI-assisted vulnerability research, automated patching, attack-path analysis, and resilient infrastructure will be substantially better positioned to handle machine-speed exploitation.
(+1) Vulnerability remediation will increasingly become an automated, risk-based process rather than a manually managed backlog, with critical exposures receiving near-real-time treatment.
(+1) Security teams will increasingly measure themselves by remediation latency and exposure reduction instead of simply counting vulnerabilities closed.
(+1) AI will become a major defensive force as organizations use the same advances in code analysis and vulnerability discovery to identify weaknesses before attackers can weaponize them.
(-1) Organizations that continue relying on periodic scans, enormous remediation backlogs, manual ticketing, and slow maintenance cycles will face growing exposure as attackers gain faster AI-assisted discovery and exploitation capabilities.
(-1) The patch gap is likely to become more dangerous for organizations that cannot automate testing and deployment, particularly when newly disclosed vulnerabilities can be converted into working exploits rapidly.
(-1) Security programs that treat CVSS as their primary prioritization mechanism may increasingly struggle to distinguish technically severe vulnerabilities from the exposures that actually represent the greatest business risk.
The Bottom Line
The future of vulnerability management is not simply about finding more vulnerabilities.
It is about understanding which exposures matter, how quickly they can become dangerous, and how rapidly the organization can remove them.
Frontier AI is accelerating the offensive side of cybersecurity, but it can also accelerate defense.
The critical decision for security leaders is whether their organizations will use that acceleration to modernize their vulnerability programs—or continue operating according to a security timetable designed for a slower era.
The machine-speed era is already beginning.
The vulnerability-management program that survives it will be the one capable of moving just as quickly.
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