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The New CCNA v2.0 Exam May Be Significantly Harder Than You Think
What is Changing in the CCNA 200-301 v2.0 Exam?The CCNA 200-301 v2.0 exam places greater emphasis on troubleshooting, operational analysis, scenario-based assessment, security, and artificial intelligence. It is scheduled to become available on February 3, 2027.For years, Cisco’s CCNA certification has served as the launch point into professional networking. Historically, the exam focused heavily on helping learners understand:What networking technologies doWhy they existBasic implementation conceptsBasic monitoring and verificationEntry-level Cisco IOS configurationTroubleshooting certainly existed in the previous CCNA blueprint, but more often than not, learners could succeed by developing a reasonably solid conceptual understanding of networking technologies and learning how to configure and verify them at a foundational level.I believe the new CCNA v2.0 exam changes that substantially–and many learners may not yet realize how significant that change actually is.The Shift from “Technology Familiarity” to “Operational Understanding”When you compare the previous CCNA v1.1 blueprint to the newly released CCNA v2.0 blueprint, one pattern becomes immediately obvious: the language of the exam objectives has shifted heavily toward troubleshooting, interpretation, diagnosis, and operational analysis.The new blueprint repeatedly uses terms such as:TroubleshootDiagnoseInterpretValidateThis is not accidental wording. Cisco appears to be shifting the CCNA away from being primarily a technology-recognition and introductory implementation exam and toward an operational networking exam, a very important distinction.Why Troubleshooting Changes EverythingMany learners underestimate how much more difficult troubleshooting actually is compared to learning basic configuration.There is an enormous difference between: “What does OSPF do?” and “Why is this OSPF adjacency failing?”The first question can often be answered through memorization. The second question requires deep understanding.To troubleshoot successfully, learners must understand:What a protocol is supposed to doWhy it was designedHow it operates internallyWhat dependencies it hasWhat conditions cause failureHow to isolate root causesHow to interpret operational outputsThat is an entirely different cognitive level.In other words: the exam appears to be shifting from conceptual familiarity toward operational competency.Cisco is Clearly Preparing Learners for an AI-Assisted FutureAnother major signal in the new blueprint is Cisco’s obvious movement toward AI-assisted network operations. The exam now explicitly references:Agentic AIGenerative AIPrompt engineeringAI-assisted operational workflowsThis is not simply Cisco adding a “buzzword section” to the exam. I believe Cisco is acknowledging what is already beginning to happen inside enterprise networking environments: AI systems will increasingly monitor networks continuously.These systems will:Analyze telemetryMonitor traffic flowsDetect anomaliesIdentify outagesCorrelate eventsRecommend remediation stepsPotentially automate portions of incident responseIn many environments, the AI system may detect the problem long before a human administrator notices anything is wrong. That future is coming rapidly.AI Does NOT Eliminate the Need for Deep Networking KnowledgeThis is where I think many learners may misunderstand the role AI will play.Some people hear “AI-assisted networking” and incorrectly assume: “The AI will just troubleshoot everything for me.” That is a dangerous assumption.Even if AI tools identify problems and recommend remediation actions, the Network Administrator still must determine:Will this remediation actually solve the issue?Could this remediation introduce new problems?Is the AI correctly identifying the root cause?Does the recommendation align with business requirements?Could this create outages elsewhere?Could this impact security or compliance?Is the recommendation appropriate for this environment?Those decisions require real engineering knowledge. In some networks, mistakes can be catastrophic. This becomes especially important in environments such as:HealthcareEmergency servicesFinancial systemsIndustrial control systemsMilitary networksThese are not environments where an administrator can blindly trust automation. Network failures in some environments can literally become life-or-death situations.Would you feel comfortable allowing an AI platform to automatically make major routing, security, segmentation, or access-control changes in a military or hospital network without human oversight?Most experienced engineers probably would not, and I strongly suspect Cisco understands that.AI Changes the Engineer’s Role — It Does Not Remove ItWhat I believe Cisco is preparing learners for is a future where network engineers increasingly function as:ValidatorsAnalystsDecision-makersRisk evaluatorsOperational overseersrather than simply CLI typists manually configuring devices all day. In that world, understanding why the AI is recommending a particular action becomes critically important.If the AI recommends:changing an OSPF costdisabling a switch portmodifying an ACLshutting down an interfacererouting trafficquarantining endpointsthe engineer must still understand the technical implications of those actions. That requires deep operational knowledge.
The “Removed Topics” Are Not Necessarily GoneOne of the most misleading things about the new blueprint is that several explicit topics disappeared from the objective list. Some learners may incorrectly assume that means those concepts no longer matter. I believe that would be a serious mistake.For example:The new blueprint no longer explicitly lists IPv6 address types in detail; however, the exam still includes:IPv6 troubleshootingIPv6 prefix sizingOSPFv3You cannot realistically troubleshoot OSPFv3 or IPv6 addressing issues without understanding:Link-local addressesIPv6 formattingPrefix interpretationAbbreviation rulesAddress scopeSimilarly, the blueprint no longer explicitly lists MAC learning and frame flooding. But Layer 2 troubleshooting absolutely depends on understanding:MAC tablesFlooding behaviorSwitch forwarding logicIn other words: Some foundational concepts may no longer be explicitly stated but still appear operationally necessary to understand the technologies that remain.Scenario-Based Testing Will Likely Increase DifficultyThe new exam description heavily emphasizes scenario-based testing. That matters.Scenario-based questions are dramatically different from simple recall questions. These questions typically require learners to:Read lengthy scenariosInterpret operational symptomsIgnore irrelevant informationIdentify misleading detailsCorrelate multiple technologiesDetermine the actual root causeSelect the best answer among multiple technically plausible choicesThese questions consume far more time than traditional multiple-choice questions. This may also create additional difficulty for non-native English speakers, especially if the exam is not available in their native language. Technical knowledge alone may no longer be sufficient. Learners may also need strong reading-comprehension and analytical skills under time pressure.The AI Objectives are More Important than Many Learners RealizeOne of the most overlooked additions to the blueprint is this objective:“Select a prompt to send to a generative AI system to support network operations considering prompt components such as data classification, output format, persona, and instructions.”This is a very modern objective. And for many networking learners, terms such as:PersonaOutput formatData classificationStructured outputMachine-consumable outputmay be completely unfamiliar.The good news is that these concepts can actually be learned relatively quickly. The bad news is that learners who ignore them may struggle significantly on these objectives.AI Prompt Engineering is Now a Networking SkillI strongly recommend that CCNA learners begin actively practicing AI prompt engineering using tools such as ChatGPT. Not casually. Intentionally.For example, learners should practice generating:Structured YAML filesJSON outputsMarkdown documentationAutomation templatesTroubleshooting workflowsAPI payloadsAnsible playbooksand they should learn how prompt wording affects output quality. This is no longer “future technology.” Cisco has now placed AI prompt design directly into the CCNA blueprint–that should get everyone’s attention.One of the Best Study Techniques for the New CCNAOne of the most powerful things learners can now do is use AI itself to generate complex scenario-based practice questions.For example, I would personally use prompts similar to:Generate troubleshooting scenariosCreate operational analysis questionsProduce realistic Cisco CLI outputsSimulate broken configurationsGenerate misleading distractorsCreate machine-consumable automation tasksThe goal is no longer simply memorization. The goal is operational reasoning.My Recommendation to Future CCNA LearnersIf you are preparing for the new CCNA v2.0 exam, my advice is simple: Do not study merely to memorize technologies.Study to understand:why protocols existwhat problems they solvehow they operate internallyhow they failhow to isolate failureshow to interpret outputshow technologies interactBecause in the AI-assisted future Cisco is clearly moving toward, the engineer who succeeds will not simply be the person who can type commands.It will be the person who can correctly evaluate:what the AI is recommendingwhy it is recommending itwhether the recommendation is technically soundwhether it could create unintended consequencesand whether it is truly appropriate for the operational environmentThat is a far more advanced skillset than memorization. Based on the new blueprint, I believe Cisco fully intends the new CCNA to begin developing exactly those skills.How INE Is Preparing Learners for CCNA v2.0INE currently offers a complete CCNA Learning Path consisting of videos, quizzes, and hands-on labs designed to help learners prepare for the current CCNA 200-301 v1.1 certification exam.Over the next several months, we will be updating that Learning Path to account for the changes introduced in the upcoming CCNA 200-301 v2.0 certification exam, which Cisco has announced will first become available on February 3, 2027.However, learners should absolutely not assume that the current Learning Path has suddenly become obsolete. In fact, the vast majority of the technologies and operational concepts taught throughout the existing CCNA Learning Path remain highly relevant to the upcoming v2.0 exam and still provide critical foundational knowledge necessary to succeed. Additionally, our current CCNA Learning Path already includes materials specifically designed to help learners strengthen troubleshooting skills.For example, we currently offer a course titled CCNA 200-301 Practice Labs. Within that course are ten dedicated “CCNA Troubleshooting Labs” designed to help learners move beyond simple memorization and begin developing the deeper operational reasoning skills that appear to be heavily emphasized in the upcoming v2.0 blueprint.Those troubleshooting labs help learners practice:identifying root causesinterpreting operational outputsanalyzing broken configurationsunderstanding protocol behaviorcorrelating symptoms with failuresand developing structured troubleshooting methodologies—all of which appear increasingly important in the direction Cisco is taking the CCNA certification.So while the exam blueprint is evolving, the core networking knowledge and troubleshooting foundations learners are building today remain extremely valuable and continue to form the basis for success in both the current and upcoming versions of the CCNA exam.
FAQsWhen does the CCNA v2.0 exam launch?The CCNA 200-301 v2.0 exam is scheduled to launch on February 3, 2027. The current CCNA v1.1 exam remains available through February 2, 2027.Will the CCNA v2.0 exam be harder?The new exam may feel harder because it emphasizes troubleshooting, interpretation, practical assessments, AI, and operational decision-making rather than relying primarily on conceptual recall.What new topics are included in CCNA v2.0?Notable additions and expanded areas include generative AI, agentic AI, prompt engineering, scenario-based analysis, security, and additional troubleshooting tasks.Should I wait for CCNA v2.0 before getting certified?Learners already preparing for the current exam should generally continue. Cisco recommends staying on track because the foundational skills developed for v1.1 remain relevant to v2.0.How should I prepare for the new CCNA exam?Learners should combine conceptual study with hands-on configuration, troubleshooting labs, interpretation of Cisco IOS outputs, scenario-based questions, and intentional practice using generative AI tools.Does AI reduce the need to learn networking fundamentals?No. AI may help identify problems or suggest remediation, but network professionals must still validate the recommendation, assess risk, understand dependencies, and determine whether a change is appropriate for the environment.
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AI Security Readiness Is Now an Operational Skill Beyond the Engineering Team
Generative AI did not wait for enterprise AI strategies to mature before entering the workplace.Employees began using chat-based AI tools to summarize documents, troubleshoot issues, draft code, analyze tickets, write reports, and accelerate daily work before many organizations had clear policies, visibility, controls, or training programs in place. That created risk from the beginning: sensitive data could be pasted into public tools, AI-generated answers could be trusted too quickly, and business users could introduce AI into workflows before IT, security, legal, or compliance teams understood where data was going or how outputs were being used.Today, those risks are not brand new. They are evolving.AI is moving from informal use into structured enterprise workflows. Chat assistants are being connected to internal data. Retrieval-augmented generation systems are searching across knowledge bases. AI-enabled tools are supporting security operations, software development, cloud administration, IT support, reporting, and automation. Agents are being granted more access to systems, APIs, and workflows.That creates a new security reality: AI risk is no longer limited to the people building models. It belongs to the people using, connecting, monitoring, and securing AI-enabled systems every day.That is why AI security readiness matters.What is AI security readiness?AI security readiness is the operational capability to identify, assess, test, mitigate, and continuously monitor security risks associated with AI-enabled systems.It is not the same as general AI literacy. AI literacy helps users understand what AI is, how to use it responsibly, and where broad risks may exist. AI security readiness goes further. It helps technical practitioners understand how AI systems behave in real environments, where security assumptions can fail, and how to validate controls around prompts, retrieval layers, tools, outputs, logging, permissions, and human review.In practical terms, AI security readiness means a team can answer questions like:What data can this AI system access?Where can sensitive information leak?Could a prompt injection attack alter system behavior?Are retrieval results properly authorized, scoped, filtered, and validated?What tools, APIs, or actions can the AI system trigger?Where does human approval need to be required?How will the team test whether controls are working?Those are not abstract AI questions. They are operational security questions.The risk started with adoption. It is expanding with integration.Many organizations initially treated generative AI as a productivity issue. Employees were using chatbots, copilots, and assistants to move faster, often outside formal technology review processes. The early risks were immediate and practical: sensitive data exposure, unverified outputs, shadow AI use, weak guidance, and limited logging or oversight.Now the risk surface is expanding because AI is becoming more integrated.A chatbot used by one employee creates one type of risk. A retrieval system connected to internal documents creates another. An AI agent that can open tickets, query databases, call APIs, summarize alerts, or trigger automation introduces a different level of exposure.The shift is not from “safe” to “risky.” The shift is from unmanaged individual use to embedded operational dependence.That distinction matters. Organizations do not need panic. They need maturity.Why AI security is no longer only an engineering problemMany organizations began formal AI programs as innovation projects. A small technical group evaluated tools, tested use cases, reviewed vendors, and explored proofs of concept. But that model doesn't fully reflect how generative AI is being used across the business.AI-enabled workflows appear across IT support, SOC operations, software development, cloud operations, internal knowledge management, ticketing, reporting, and automation.A help desk technician may use AI to summarize an incident. A SOC analyst may use AI to enrich an alert. A developer may use AI to generate or review code. A cloud engineer may use AI to draft automation. A business user may use an AI assistant to search internal documents.Each workflow introduces different security questions.A SOC analyst does not need to become an AI researcher to use AI safely. But that analyst does need to understand why AI-generated enrichment should be verified before escalation. A cloud operations professional does not need to build a language model from scratch. But that professional does need to understand how tool permissions, secrets, logs, and human approval gates affect AI-enabled automation.This is the practical middle ground organizations need to build.INE’s AI Systems Security Specialist (eAIS) Certification is designed for that exact gap: foundational, role-aligned skills for identifying, testing, and securing AI-powered systems. The certification is built for IT support teams, help desk professionals, system administrators, junior security analysts, SOC teams, DevOps, platform and cloud operations professionals, students, and career changers entering cybersecurity.Where traditional approaches fall shortMost organizations already have some combination of cybersecurity awareness, acceptable-use policies, security tooling, and application security processes. Those remain important. They are not enough on their own.AI awareness training can explain policy, ethics, and safe-use expectations. But awareness does not teach a practitioner how to test whether a retrieval-augmented generation system is exposing sensitive documents or whether an AI agent has too much permission.Traditional application security can help identify code-level and architecture-level weaknesses. But AI-enabled systems introduce behavior that depends on prompts, retrieved context, model responses, tool access, logging, and downstream interpretation.Advanced AI red teaming is valuable for mature programs and specialized teams. But many organizations need a broader baseline first. They need IT, SOC, DevOps, cloud, and security practitioners who can recognize common AI-specific risks and apply practical controls before escalation.That baseline is quickly becoming part of operational maturity.Practical AI security risks teams need to recognizeAI security readiness starts with knowing what can go wrong in real workflows.Sensitive data exposure can happen when employees paste confidential information, customer data, source code, credentials, incident details, or regulated data into AI systems without understanding retention, logging, or access implications.Prompt injection occurs when crafted instructions manipulate an AI system into ignoring rules, revealing information, or taking unintended actions. This can happen directly through user prompts or indirectly through content the AI system retrieves or processes.RAG exposure can happen when retrieval systems surface documents, embeddings, or context that users should not access. A system may appear secure at the interface while still retrieving from poorly scoped data sources behind the scenes.Tool misuse becomes a risk when AI systems can call APIs, query databases, create tickets, execute commands, send messages, or trigger automations. The more agency a system has, the more important least privilege, validation, and human approval become.Unsafe logging and retention can create new exposure paths when prompts, outputs, embeddings, chat histories, ticket summaries, or troubleshooting records store information that should not be retained or broadly accessible.Overreliance becomes an operational risk when teams accept AI outputs without review. In security operations, development, and IT workflows, a confident answer is not the same as a verified answer.These risks do not mean organizations should avoid AI. They mean AI adoption has to be paired with practical security skills.How eAIS helps validate AI security readinesseAIS gives practitioners and teams a structured path for building and validating foundational AI security skills.The certification focuses on practical operational readiness, including AI system architecture, exposure points, prompt injection, AI abuse techniques, defensive controls, AI security testing, validation, and safe operational use.That matters because organizations need more than policy acknowledgment. They need evidence that practitioners understand how AI systems change risk and can apply controls in realistic scenarios.The eAIS learning path supports preparation through expert-led instruction and hands-on training covering AI system architecture, embeddings and RAG security, prompt injection and jailbreaks, tool and agentic workflow abuse patterns, defensive controls, secure integrations, human-in-the-loop automation, telemetry, and AI security testing.For enterprise teams, INE Enterprise supports workforce development through hands-on labs, assessments, reporting, team management, analytics, and practical learning paths that help leaders track progress and performance across teams.For SOC teams specifically, eAIS can also complement defensive operations training. INE’s cybersecurity training supports practitioners building skills across security operations, incident response, threat hunting, penetration testing, cloud security, and related domains. Together, AI security readiness and core SOC skills help organizations prepare analysts for both traditional security operations and the AI-enabled workflows increasingly appearing inside modern SOCs.What organizations should do nextAI security readiness should become part of workforce planning, not an afterthought added after AI adoption accelerates.Security leaders can begin by identifying where AI is already used across IT, SOC, DevOps, cloud, and development workflows. From there, teams should map the highest-risk AI-enabled processes, define baseline controls, and determine which roles need practical AI security training.A useful first step is to separate three groups:General users need acceptable-use guidance and awareness.Technical operators need practical AI security readiness: how to recognize AI-specific risk, apply baseline controls, test behavior, and use AI safely in operational workflows.Specialists need deeper AI red teaming, architecture review, governance, and advanced testing capabilities.Most organizations will need all three layers. The urgent gap is the middle one, where many real-world AI risks will first be noticed, handled, or missed.The future of AI security is operationalAI security will continue to evolve quickly. New tools, architectures, attack techniques, and governance expectations will keep changing how organizations assess risk. But the underlying workforce challenge is already clear.Organizations cannot secure AI adoption with policy alone. They also cannot rely only on a small group of specialists to review every AI-enabled workflow. AI has already entered daily work. Now security capability has to meet it there.AI security readiness turns AI risk from a vague concern into a measurable skill set. It helps practitioners understand where AI systems can fail, how attackers may exploit them, and what controls can reduce risk. It helps leaders move from AI enthusiasm to AI governance with operational confidence.For organizations building that capability, INE’s eAIS Certification offers a practical starting point for validating foundational AI systems security skills. Teams looking to scale training across roles can also explore INE Enterprise or talk to an INE Team Advisor about workforce readiness in the AI era.
FAQWhat is AI security readiness?AI security readiness is the operational capability to identify, assess, test, mitigate, and continuously monitor security risks associated with AI-enabled systems. It includes understanding how AI systems use prompts, retrieval, tools, data, outputs, logs, permissions, and human review so teams can apply practical controls and validate that those controls work.Are generative AI security risks new?No. Generative AI introduced practical risk as soon as employees began using widely available AI chatbots in daily work. What is changing now is the level of enterprise integration, data access, automation, and autonomy. The risks are not new, but they are evolving.Who needs AI security training?AI security training is useful for security analysts, SOC teams, IT support, help desk professionals, system administrators, DevOps teams, platform engineers, cloud operations teams, junior cybersecurity professionals, and anyone responsible for using or securing AI-enabled workflows.How is AI security different from AI literacy?AI literacy helps users understand AI concepts, acceptable use, and broad risks. AI security focuses on practical risk reduction: identifying prompt injection, data leakage, RAG exposure, tool misuse, unsafe outputs, excessive permissions, and control failures in real workflows.Why do SOC, IT, DevOps, and cloud teams need AI security skills?These teams increasingly use AI in operational workflows and may also support systems that connect AI to data, tools, logs, infrastructure, and automation. AI security skills help them verify outputs, protect sensitive data, apply least privilege, test controls, and prevent unsafe AI-assisted actions.What does the eAIS Certification cover?INE’s AI Systems Security Specialist (eAIS) Certification focuses on foundational AI security readiness, including AI system architecture, prompt injection, RAG security, tool and agentic workflow risks, defensive controls, testing, validation, and safe operational use.Is eAIS only for AI engineers?No. eAIS is designed for technology and operations professionals who may not build AI models but increasingly work with AI-enabled systems. That includes IT support, help desk, system administrators, SOC analysts, DevOps, platform, cloud operations, students, and career changers entering cybersecurity.How can enterprises build AI security readiness across teams?Enterprises can begin by identifying where AI is already used, mapping high-risk workflows, defining baseline controls, and training technical operators who support AI-enabled systems. INE Enterprise can help teams scale hands-on training, assessments, learning paths, reporting, and progress tracking across cybersecurity and IT roles.
INE Launches eAIS Certification to Build Practical AI Security Readiness
New AI Security Fundamentals credential focuses on prompt injection, RAG security, tool misuse, and safe operational use of AI across IT and cybersecurity teamsCARY, N.C. —June 23, 2026 — INE, a leading provider of cybersecurity and networking training, today announced the launch of AI Systems Security Specialist (eAIS), a new certification designed to help IT, cybersecurity, and operations professionals understand, assess, and secure modern AI-enabled systems.As organizations rapidly adopt copilots, chat assistants, LLM-enabled applications, retrieval-augmented generation (RAG), and agentic workflows, AI security is moving beyond a specialized engineering discipline and becoming a frontline responsibility. eAIS is built for technology and operations professionals who are not AI engineers, but are increasingly expected to identify AI-specific risk, apply baseline controls, and use AI safely in day-to-day operational environments.The certification addresses a growing gap between general AI literacy and advanced AI red teaming. eAIS focuses on the practical middle ground: helping practitioners recognize how AI systems change risk, where sensitive data can leak, how attackers exploit AI workflows, and how to validate that foundational defenses are working.“With eAIS, we are giving practitioners a practical, security-first credential that helps them understand AI risk, apply effective controls, and make safer decisions in real operational environments. For enterprises managing AI adoption this is a game-changer in assessing and validating needed skills.” said Lindsey Rinehart, CEO of INE. Built for Practical AI Security ReadinesseAIS is designed for IT support teams, help desk professionals, system administrators, junior security analysts, SOC teams, DevOps, platform, and cloud operations professionals, as well as students and career changers entering cybersecurity.The certification emphasizes security-first AI fundamentals, including:Understanding how LLM applications, RAG pipelines, tools, agents, and logging systems create new exposure pointsIdentifying prompt injection, jailbreaks, data leakage, RAG poisoning, tool misuse, and over-permissioning risksApplying practical safeguards such as least privilege, parameter validation, fail-closed controls, human-in-the-loop gates, structured outputs, retrieval hardening, and safe loggingTesting and validating mitigations through repeatable, hands-on exercisesUsing AI safely in SOC, IT operations, automation, and SDLC workflows through verify-before-act practices, audit trails, review gates, and secrets hygieneUnlike broad AI literacy programs, eAIS is vendor-neutral, tool-agnostic, and grounded in operational security outcomes. Learners are trained not only to understand AI risk, but to implement and verify controls in ways that align with real enterprise environments.Comprehensive Coverage Across AI Security DomainsThe learning path includes focused modules on AI systems for security practitioners, embeddings and RAG security, prompt injection and jailbreaks, tool and agentic workflow abuse patterns, defensive controls, secure integrations, human-in-the-loop automation, telemetry, and AI security testing.For enterprises, eAIS provides a clear signal that teams can safely evaluate AI-enabled workflows and apply baseline controls before risk becomes operational exposure. For practitioners, it offers a practical credential for building confidence in one of cybersecurity’s fastest-growing areas.AvailabilityThe AI Security Fundamentals (eAIS) certification and learning path are available now through INE. For details on exam requirements, preparation resources, and enterprise training options, visit ine.com/enterprise.
AI Is Finding More Vulnerabilities Than Teams Can Fix — Here’s the Real Challenge
AI is changing cybersecurity operations faster than most organizations can adapt.Security teams now have access to tools that can scan codebases, identify weaknesses, surface suspicious behaviors, and accelerate investigations at unprecedented scale. Tasks that once required days of manual effort can now happen in minutes.On the surface, that sounds like progress.But for many organizations, the result has been a growing operational problem: more findings, more alerts, and more decisions than teams can realistically process.The challenge is no longer visibility.It’s prioritization, validation, and operational readiness.More Visibility Doesn’t Automatically Reduce RiskAI-powered security tooling has dramatically increased the volume of information security teams can access.Teams can now:Analyze larger environments fasterDetect patterns humans may missSurface vulnerabilities at scaleAutomate portions of research and analysisBut identifying issues is only part of the equation.Every finding still requires someone to determine:Is this a legitimate risk?Does it impact production systems?Is immediate action required?What are the operational consequences of remediation?Those decisions still rely heavily on human judgment, context, and experience.The operational bottleneck has shifted.Security teams are no longer struggling to see problems. They are struggling to decide what matters most.AI-Powered Systems Are Expanding the Attack SurfaceAt the same time, organizations are rapidly adopting AI-powered systems across business and technical workflows.LLM applications, AI copilots, retrieval-based systems, and autonomous agents are becoming part of everyday operations in IT, security, engineering, and customer support environments.These technologies create new efficiencies—but they also introduce new categories of risk.Security and IT teams now need to understand:How AI systems process and expose dataWhere prompts, logs, and retrieved information create exposure pointsHow prompt injection and jailbreak techniques workHow AI-enabled tools and integrations can be abusedWhat controls reduce operational risk in AI-powered workflowsFor many organizations, this represents a significant skills gap.Traditional cybersecurity training often doesn’t address AI-specific workflows and risks. At the same time, most AI education focuses on model development or productivity—not operational security.Why Traditional Approaches Are Falling ShortMany organizations are attempting to address AI-related risk through policy alone.Governance frameworks, usage restrictions, and internal guidelines are important—but they are not enough to prepare technical teams for the operational realities of AI-powered systems.Security teams need practical knowledge that helps them:Recognize AI-specific threatsValidate findings instead of blindly trusting outputsApply foundational safeguardsSafely test and evaluate AI-enabled applicationsSupport AI adoption without increasing organizational riskThis is not purely a security challenge.It’s an operational readiness challenge that affects security, IT, cloud, platform, and engineering teams alike.The Organizations Adapting FastestThe organizations responding most effectively to this shift are not necessarily the ones deploying the most AI tools.They are the ones investing in workforce readiness.Forward-looking teams are building foundational AI security capability across technical functions so employees can:Understand how AI systems behave in real environmentsRecognize where exposure and misuse can occurMake informed operational decisionsApply practical controls that reduce risk without slowing innovationThis approach improves more than security posture.Building Practical AI Security ReadinessAs AI becomes embedded across enterprise environments, organizations need professionals who can securely support, evaluate, and operate these systems in practice—not just understand them conceptually.The AI Systems Security Specialist (eAIS) learning path and certification was designed to help IT and cybersecurity professionals build foundational, hands-on skills for working securely with modern AI-powered systems.eAIS focuses on practical operational readiness, including:AI system architecture and exposure pointsPrompt injection and AI abuse techniquesFoundational controls for securing AI-powered systemsAI security testing, validation, and operational safetyThe program is designed for security analysts, IT teams, cloud and platform professionals, and organizations looking to build practical AI security capability across technical teams.Looking AheadAI will continue to accelerate how organizations detect, analyze, and respond to security challenges.But the organizations that succeed long term will not rely on automation alone.They will invest in building teams capable of understanding AI systems, evaluating risk intelligently, and making informed operational decisions in increasingly complex environments.That is where the real competitive advantage will come from.👉 Learn more about the AI Systems Security Specialist (eAIS) Learning Path and Certification
8 Must-Have Networking and Cybersecurity Skills for OT Environments
The Line Between IT and OTMost organizations focus heavily on protecting information technology (IT) systems — company networks, applications, devices, cloud infrastructure, and the sensitive data they store.Today’s most persistent cybersecurity threat in IT environments is identity and credential compromise, increasingly fueled by AI-enhanced phishing attacks. Once attackers gain access, the risk of data theft, operational disruption, and ransomware escalates quickly.Operational technology (OT) environments face a different challenge. Industries that rely on heavy machinery and physical infrastructure must prioritize safety and availability above all else — keeping the power on, production running, and critical services operational.OT systems control the physical processes behind industrial operations, including pumps, turbines, conveyors, safety systems, and industrial control systems (ICS). Unlike traditional IT environments, many OT networks were designed for reliability and uptime long before modern cybersecurity threats became a concern.As a result, legacy software, remote vendor access, and an expanding network edge of connected sensors and mobile devices can introduce significant security gaps.Professionals working in energy, utilities, manufacturing, and transportation need strong networking and cybersecurity foundations to secure these increasingly connected OT environments.
Top Networking Skills for OT SecurityStrong networking fundamentals are essential for securing modern OT environments. As IT and OT systems become more interconnected, professionals need to understand how data moves across industrial networks, how access is controlled, and how to reduce risk without disrupting operations.INE provides technical training and certification preparation across leading networking and security technologies, including Cisco, Fortinet, and more.The following networking skills help security and infrastructure teams build more resilient OT environments.
1. Network SegmentationEffective OT security starts with network segmentation. Organizations must separate corporate IT systems, industrial control networks, vendor access paths, and field devices to prevent threats from moving laterally across the environment.Proper segmentation helps contain incidents, limit unauthorized access, and protect critical operational systems without disrupting uptime.
INE Training: Enterprise Network Security PrinciplesLearn security fundamentals including attack surfaces, Layer 2 and Layer 3 threats, segmentation strategies, security zones, device hardening, and perimeter defense techniques.
2. Remote Access Controls Industrial environments often rely on legacy devices, fixed communication paths, and systems that cannot tolerate unexpected downtime or configuration changes. Because of this, security teams must carefully manage how users, vendors, and operators connect to OT systems.That includes understanding firewalls, VLANs, jump hosts, remote access policies, and traffic monitoring across both enterprise and industrial networks.Secure remote access goes beyond VPN connectivity alone. Organizations also need role-based permissions, session logging, multi-factor authentication (MFA), and visibility into traffic moving between control centers, substations, and field devices.
INE Training: Implementing Inter-VLAN Routing Learn how to implement inter-VLAN routing using Router-on-a-Stick and Switched Virtual Interfaces (SVIs) to better manage segmented network communication and traffic control.
3. Security Hardening Security hardening involves configuring systems, devices, and applications to reduce vulnerabilities while maintaining operational reliability. In OT environments, hardening is especially important because many IoT and ICS assets were not originally designed with modern cybersecurity protections in mind.Proper hardening helps reduce the attack surface across industrial systems, limit unauthorized access, and improve resilience against ransomware and other cyber threats.
INE Training: Security Engineering and System Hardening Bootcamp Learn the fundamentals of security engineering and how to properly secure common operating systems, devices, and enterprise infrastructure.
4. Software-Defined Networking (SDN) for OTSoftware-defined networking (SDN) helps organizations manage complex OT environments more efficiently and securely. By using centralized controllers and policy-based management, teams can monitor network activity, segment traffic, and apply security policies consistently across distributed industrial systems.This becomes especially valuable in remote or large-scale operations where administrators need visibility into substations, manufacturing sites, or field devices without manually configuring every network component.SDN also improves scalability by allowing organizations to prioritize critical traffic, automate network changes, and respond more quickly to operational or security issues.
INE Training: Implementing Cisco SD-WANLearn the theory and hands-on configuration, verification, and troubleshooting skills needed to deploy and manage Cisco SD-WAN solutions.
Cybersecurity Skills for OT Environments
5. SCADA and ICS Security FundamentalsYou cannot secure industrial systems without understanding how they operate. In OT environments, that starts with learning the fundamentals of industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems.Security professionals should understand the role of programmable logic controllers (PLCs), human-machine interfaces (HMIs), remote terminal units (RTUs), and distributed control systems (DCS). They also need familiarity with common industrial protocols and how data moves between sensors, controllers, and operator workstations.This foundational knowledge helps teams identify operational risks, secure critical infrastructure, and communicate more effectively with engineering and operations teams.
INE Training: Introduction to Cyber Security Hardening Learn how to securely deploy and harden systems across Windows, Linux, macOS, IoT, and ICS environments to reduce the overall attack surface.
6. Threat Detection, Logging, and Incident ResponseOT environments require continuous monitoring, log analysis, and structured incident response processes to identify and contain threats without disrupting critical operations.Security teams must understand security information and event management (SIEM) platforms, alert triage, log correlation, and threat investigation techniques across both IT and OT systems.The challenge in industrial environments is balancing speed with operational control. Before taking action, responders often need to validate the scope of an incident, analyze logs across multiple systems, and coordinate closely with engineering and operations teams to avoid unintended downtime.INE Training: SOC Logging & Analysis Learn core SIEM concepts including events, alerts, dashboards, visualizations, and practical log analysis techniques used in modern security operations centers (SOCs).
7. Vulnerability Management and Patching in Critical SystemsVulnerability management in OT environments is far more complex than routine software patching. Many industrial systems cannot be taken offline easily, making traditional patch cycles difficult or even impossible.Modern security programs are shifting away from calendar-based patching toward Continuous Exposure Management — using real-time threat intelligence to prioritize known exploited vulnerabilities (KEVs) and reduce risk based on active threats.In cases where critical assets cannot be patched immediately, organizations often rely on compensating controls such as network segmentation, virtual patching, and restricted access policies to protect legacy systems while maintaining operational uptime.The goal is not simply to patch systems quickly, but to reduce risk safely without disrupting critical operations.
INE Training: Introduction to Vulnerability Management Learn how to identify vulnerabilities using modern scanning tools, prioritize and classify risks, and build effective vulnerability management and reporting processes.
8. Cloud, Identity, and Secure Access ManagementAs OT and IT environments become more interconnected, security professionals need a strong understanding of identity and access management (IAM), multi-factor authentication (MFA), privileged access controls, and zero trust principles.Managing identity securely is especially important in industrial environments where third-party vendors, engineers, contractors, and hybrid teams may require remote access to critical systems.Organizations must carefully control who can access OT assets, what permissions they have, and how access is monitored across both on-site and remote operations. INE Training: Introduction to Identity & Access Management Learn the fundamentals of authentication, authorization, and accounting (AAA), and how these concepts support secure identity and access management practices.
Build Stronger OT Security TeamsAs industrial environments become more connected, organizations need professionals with expertise across networking, cybersecurity, and operational technology.INE helps enterprises develop the technical skills needed to secure modern OT and ICS environments through hands-on training, certification preparation, and practical cybersecurity education.Whether your teams are strengthening network segmentation, improving incident response, or building secure remote access strategies, the right technical foundation is critical to reducing operational risk.Explore INE’s networking and cybersecurity training to help your teams build safer, more resilient OT environments.
INE Helps Public Agencies Prepare for the Rise of AI-Driven Cyber Attacks
New training initiative addresses deepfakes, AI phishing, and evolving threats targeting public trust and critical servicesCARY, N.C. — June 3, 2026 - INE, global provider of networking and cybersecurity training and certifications, today announced an expanded public sector cybersecurity training initiative designed to help local governments defend against rapidly evolving AI-enabled threats targeting both human and technical systems.AI Attacks Are Eroding Trust Across Public SystemsAs AI-powered attacks become more convincing and automated, public agencies are facing a growing trust challenge on two fronts: trust in communications and trust in the systems behind them.AI-generated voice clones are being used to impersonate government officials and authorize fraudulent transfers. Hyper-personalized phishing campaigns can now mimic internal communication styles using publicly available information from social media, meeting records, and online documents. At the same time, autonomous AI tools are continuously scanning for exposed APIs, cloud misconfigurations, and unpatched legacy systems.These attacks succeed because they exploit both human judgment and technical vulnerabilities simultaneously. A fake voice can sound legitimate. A fraudulent email can appear routine. An improperly secured AI-enabled chatbot can unintentionally expose sensitive information. When incidents occur, agencies are increasingly forced to determine whether systems were breached directly, manipulated through deception, or both.“The public sector is facing a new category of cyber risk,” said Lindsey Reinhardt, CEO of INE. “AI attacks are faster, more convincing, and more scalable than traditional phishing campaigns. Local governments need training that prepares teams to recognize deepfakes, respond to AI-driven threats, and maintain critical public services during an incident.”The Operational Impact Extends Beyond Data LossThe operational impact of these attacks extends well beyond data loss. Residents may lose access to billing systems and public services. Payroll processing can be disrupted. Courts may need to reschedule hearings. Emergency response systems and transit communications can be affected. Public trust can erode quickly when essential services become unavailable.For many public agencies, the challenge is compounded by limited staffing, aging infrastructure, and increasing pressure to modernize services quickly. Attackers understand that even short disruptions can create public confusion, overwhelm internal teams, and damage confidence in local institutions. The speed and scale of AI-enabled attacks are forcing agencies to rethink not only how they defend systems, but how they maintain continuity and public trust during a crisis.Building Readiness for AI-Driven ThreatsINE’s training approach is designed around the real-world scenarios public agencies are increasingly encountering, including incident response, threat hunting, SOC readiness, cloud security, data protection, and AI-focused security awareness. The program supports teams across cybersecurity, networking, cloud, data, and IT operations with practical, hands-on preparation for emerging threats.Municipal agencies that invest in continuous training, rehearsed response procedures, and modern defensive controls are better positioned to contain attacks and recover quickly. As AI-enabled threats continue to evolve, resilience requires more than annual compliance training.INE Enterprise supports public sector organizations with scalable training across cybersecurity, networking, cloud, and AI. With more than 70 learning paths and 4,500 hands-on labs, organizations can build operational readiness across teams and strengthen their ability to protect critical services and public trust.For more information about INE’s public sector cybersecurity training solutions, visit ine.com.
About INEINE is an award-winning, premier provider of online networking and cybersecurity education, including cybersecurity training and certification. INE is trusted by Fortune 500 companies and IT professionals around the globe. Leveraging a state-of-the-art hands-on lab platform, advanced technologies, a global video distribution network, and instruction from world-class experts, INE sets the standard for high-impact, career-advancing technical education.
May 2026 CVEs: Firewall RCEs & Exchange Zero-Days
May 2026 delivered another aggressive wave of high-impact vulnerabilities, with attackers heavily targeting enterprise infrastructure, identity systems, and internet-facing services. This month’s disclosures included a critical Palo Alto firewall vulnerability under active exploitation, a Microsoft Exchange OWA zero-day added to CISA’s KEV catalog, and major risks affecting Azure DevOps, Android, and nginx environments.What makes May especially significant is the concentration of vulnerabilities impacting the technologies organizations rely on most for security, communication, and cloud operations. From perimeter firewalls and email systems to CI/CD pipelines and mobile devices, these flaws demonstrate how attackers continue to focus on high-value platforms capable of enabling broad compromise and lateral movement.Why May’s CVEs MatterSecurity infrastructure itself is under attack: Firewall and reverse proxy vulnerabilities create direct paths into enterprise networksActively exploited enterprise flaws are increasing: Exchange and PAN-OS vulnerabilities were weaponized rapidlyCloud and DevOps platforms remain high-value targets: Azure DevOps exposure raises serious software supply chain concernsMobile enterprise risk continues to grow: Android vulnerabilities increasingly impact corporate identity and MFA workflowsLegacy exposure remains dangerous: The nginx flaw reportedly persisted undetected for nearly 18 yearsTogether, these vulnerabilities reinforce the growing importance of proactive patching, attack surface reduction, and visibility across hybrid enterprise environments.1. Palo Alto PAN-OS Unauthenticated Root RCE (CVE-2026-0300)Impact: Unauthenticated Root Remote Code Execution
Severity: Critical (CVSS 9.3)
Status: Actively exploited in the wildCVE-2026-0300 is one of the most serious enterprise infrastructure vulnerabilities disclosed in May 2026, affecting Palo Alto PAN-OS firewalls. The flaw exists within the User-ID Authentication Portal (Captive Portal) and allows attackers to execute arbitrary code remotely as root without authentication.Palo Alto linked exploitation activity to a suspected state-sponsored threat cluster identified as CL-STA-1132.Why it matters:Targets internet-facing firewall infrastructureEnables full perimeter compromiseAllows credential harvesting and lateral movementAttackers can deploy tunneling tools and destroy logsSecurity boundaries themselves become compromisedRecommended Actions:Patch affected PAN-OS systems immediatelyDisable exposed captive portals if not requiredReview logs for:ReverseSocks5 activityEarthWorm tunnelssuspicious nginx worker crashesMonitor for unusual outbound traffic patterns2. Microsoft Exchange OWA XSS Zero-Day (CVE-2026-42897)Impact: Session Hijacking and Mailbox Compromise
Severity: High/Critical operational impact
Status: Actively exploitedCVE-2026-42897 is a cross-site scripting (XSS) vulnerability affecting Microsoft Exchange Server Outlook Web Access (OWA). The flaw allows attackers to send specially crafted emails that execute malicious JavaScript when opened in OWA sessions.The vulnerability was rapidly added to CISA’s Known Exploited Vulnerabilities (KEV) catalog due to active exploitation activity.Why it matters:Exchange remains a top enterprise attack targetEnables credential theft and mailbox compromiseCan facilitate phishing pivoting and persistenceEmail remains a primary ransomware initial-access vectorExploitation can spread rapidly across organizationsRecommended Actions:Enable Exchange Emergency Mitigation Service (EMS)Restrict public OWA exposure where possibleRun Microsoft EOMT mitigation scriptsMonitor mailbox activity for anomaliesReview suspicious login and forwarding rule activity3. Azure DevOps Information Disclosure (CVE-2026-42826)Impact: Exposure of Secrets, Tokens, and Pipeline Data
Severity: Critical (CVSS 10.0)
Status: Patched in May 2026 Patch TuesdayCVE-2026-42826 is a critical information disclosure vulnerability affecting Azure DevOps. The flaw drew major attention due to its maximum CVSS score and the sensitive nature of DevOps environments.Azure DevOps systems frequently store deployment credentials, cloud secrets, CI/CD tokens, infrastructure configurations, and source code — making them highly valuable targets.Why it matters:Potential exposure of sensitive cloud credentialsIncreased software supply chain compromise riskCould enable malicious CI/CD pipeline injectionsMay facilitate broader cloud environment takeoverImpacts a core enterprise DevOps platformRecommended Actions:Patch affected systems immediatelyRotate potentially exposed tokens and secretsAudit build pipelines for unauthorized modificationsReview access logs for abnormal retrieval activityValidate least-privilege access policies4. Android System RCE (CVE-2026-0073)Impact: Remote Code Execution on Mobile Devices
Severity: Critical
Status: Included in Google May 2026 Android Security BulletinCVE-2026-0073 affects the Android System component and allows remote code execution under certain conditions across Android 14, 15, and 16 devices.As mobile devices continue to serve as critical identity and access points for enterprise environments, Android vulnerabilities carry growing operational and security implications.Why it matters:BYOD environments expand exposureMobile devices often store corporate credentialsMFA apps can become interception targetsCompromised devices can act as enterprise footholdsEnterprise mobile risk continues to increaseRecommended Actions:Enforce the May 2026 Android patch levelBlock outdated devices through MDM policiesRequire device compliance validationReview mobile EDR and authentication alertsRestrict access from non-compliant devices5. “NGINX Rift” Heap Buffer Overflow (CVE-2026-42945)Impact: Potential Remote Compromise of Web Infrastructure
Severity: Critical
Status: Newly disclosed; exploit-chain concerns growingCVE-2026-42945, dubbed “NGINX Rift,” is a heap buffer overflow vulnerability affecting nginx builds dating back to 2008. Researchers warned the flaw may be chainable with other Linux vulnerabilities to achieve stealthy root-level compromise.Because nginx powers a massive portion of modern web infrastructure, the disclosure immediately raised concerns across cloud-native environments.Why it matters:Affects one of the world’s most deployed web serversMay enable stealthy persistence and root accessCreates potential reverse proxy takeover scenariosImpacts Kubernetes ingress and cloud-native stacksLong-standing flaws increase exposure uncertaintyRecommended Actions:Patch nginx deployments immediately once fixes are availableReview reverse proxy configurations and exposureMonitor for suspicious memory and process activityAudit Kubernetes ingress environmentsConduct forensic reviews for persistence indicatorsFinal ThoughtsMay 2026 reinforced a growing trend in cybersecurity: attackers are increasingly targeting the platforms organizations trust most to secure, manage, and operate their environments. Firewalls, email infrastructure, DevOps pipelines, mobile devices, and reverse proxies all became focal points this month, demonstrating how a single exploited vulnerability can rapidly cascade into enterprise-wide compromise.The combination of actively exploited flaws, supply chain exposure, and internet-facing infrastructure risks highlights the need for organizations to prioritize:Rapid patch management for critical systemsVisibility across cloud, mobile, and hybrid environmentsMonitoring for exploitation activity and persistenceStrong segmentation and least-privilege access controlsContinuous validation of security infrastructure itselfAs threat actors continue to weaponize vulnerabilities faster than ever, organizations need defenders who can identify, prioritize, and respond to emerging threats in real time.👉 Train with INE to build hands-on cybersecurity expertise in vulnerability management, threat detection, cloud security, penetration testing, and incident response — helping your team stay prepared for today’s evolving threat landscape.
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