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Whether you need support for your internal audit team or a hands-off solution, Wipfli can help. Our audit team brings extensive experience and an advisory mindset to help you identify your true risk and develop a tailored internal audit plan. We provide guidance — not just a checklist — for remediation so that you can determine what approaches and solutions suit your organization’s challenges and goals.
We can also support your organization with quality assurance reviews for internal audits. We work with your team to help gain insights into performance and the value of your internal audit function.
Wipfli has supported a wide range of organizations and environments with our ISO 27001 audit readiness services. We focus on identifying gaps, strengthening controls and helping ensure your information security program aligns with certification requirements. As your partner, we work closely with your team to understand your challenges so you’re fully prepared to present policies, procedures and evidence to certifiers with confidence.
As Receivables Management Association International (RMAI)-authorized audit providers, Wipfli understands the unique regulatory demands of the receivables industry. Our compliance professionals bring concentrated financial services industry knowledge and experience to assess your organization’s CRB and CRV certification readiness and complete your compliance audit.
Help ensure compliance with the Payment Card Industry (PCI) Data Security Standard with Wipfli. Our cybersecurity team doesn’t just help you identify gaps against standards, we also apply targeted solutions to help you maintain a secure environment.
As one of the longest-tenured HITRUST assessor firms, Wipfli is ready to help you define an accurate scope, identify controls and develop targeted solutions for any gaps. We work closely with your organization, taking time to understand your needs and guiding you through each step in the process.
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Technology risk management: How to modernize while successfully managing risk
More businesses than ever are embracing a digital transformation. Cloud-based systems, big data and AI are creating a flood of new opportunities to operate more effectively and better understand your customers. But is your business prepared to tackle the new risks that emerge when it shifts onto a more digital footing? Or are you taking on unnecessary financial, operational and reputational dangers? Keep reading to learn more about why your business needs to actively manage technology risk and how to get started. What is technology risk management (TRM)? Technology risk management focuses on managing and reducing risks that involve digital systems, data or implementing new technologies. This is a big picture branch of enterprise risk management that includes more focused risk areas like cybersecurity or AI, without being limited to them. Technology-related risks include practical, operational realities like whether your systems actually function as they should. Vendor management and regulatory issues are also important, as is data integrity. Your competition may also represent a technology risk to consider. Technology can be a growth driver that helps differentiate your business from competitors or make it easier to uncover new market opportunities. But it can also leave you falling behind if your competitor wields it more effectively. Technology risk management involves taking all these factors into account and implementing a strategy to mitigate them. Why technology risk management matters during digital transformation When a business goes through a digital transformation that involves transitioning its core systems and data into the cloud, it suddenly faces a host of new risks. A thoughtful technology risk management strategy can make this process safer and more effective, while also mitigating ongoing risks. Technology investments are becoming business-critical Technology risk management is more essential now than ever before — because technology itself is more deeply integrated into virtually every business. Ten years ago, you didn’t have to be in the cloud, but now, any organization larger than a lemonade stand knows it needs modern digital systems to compete. As cloud-based technology becomes critical to your day-to-day operations, you can’t afford to write off technology risks as just cybersecurity. You’ll leave your business too exposed. Digital transformation introduces new operational risks To understand the breadth of technology risks many businesses now face, consider the example of a community bank. For fifty years, this bank has thrived even as large national banks have opened nearby branches because it offers superior customer service and has built deep lending relationships with local homeowners and small businesses. Until now. In recent years, customer expectations have shifted wildly, with younger customers (and many older ones as well) preferring to do everything online . That superior customer service doesn’t mean what it used to if the bank’s app is clunky and hard to use, so the bank scrambles to find a vendor that can clean up its customer-facing technology to avoid losing market share. But is that vendor secure enough to meet banking industry regulatory requirements? Is it reputable and audited? Will the bank itself be able to improve its digital experience enough to meet today’s customers where they are without spending money it doesn’t have? Does it have the data foundation in place to learn enough about those customers to keep them happy while also establishing enough AI governance to avoid exposing their data to public AI models? That’s a lot of new risk from many different angles. Risk visibility leads to better investment decisions Some good news: Technology risk management isn’t just about avoiding trouble. It’s also a way to make smarter business decisions that drive growth and ROI. Governance — the ongoing process of understanding how your business uses technology and creating guardrails to manage the associated risks — is a fantastic opportunity to think about how technology can improve your daily operations. You can’t set up controls if you don’t know how things work; once you know how things work, you can find new efficiencies. Governance also increases the reliability of your technology-related outputs. For example, an AI policy to mitigate bias, hallucinations and data quality risks will make answers from AI models you use more accurate, delivering clearer, more useful insights about your customers, operations and financials. In other words, managing technology risks helps you learn more about your business and your market. That information makes it easier to compete. What are common technology risks business leaders face? Businesses face a wide range of technology risks. These include risk areas like platform implementations, cloud migration, third-party vendors, AI and cybersecurity, as well as broad strategic risks like falling behind your competition in how you use technology. ERP implementation risk More businesses are transitioning to cloud-based enterprise resource planning (ERP) platforms to manage their core operations and financials. This can be a big boost for your operational efficiency, but it also carries significant risks . During the transition, you’re moving some of your most critical data from one database to another. Incorrect data mapping, poorly designed processes or corrupted data can blind your team to what’s happening inside your business. Fixing this can be expensive and also create ripple effects that disrupt your operations or carry over into your customer experience. Cloud migration risk Moving to the cloud just means transitioning from running your own servers to using servers operated by a vendor. The benefits are huge: You have all the server space you could ever need, without the worry of protecting and maintaining an in-house server bank. However, don’t assume all cloud service vendors handle those responsibilities appropriately. Sloppy server maintenance can open the door to cybersecurity issues, outages and other disruptions that can interfere with your business or create costs. Third-party technology risk Beyond cloud servers, you’re relying on vendors for your ERP, CRM, AI tools and other SaaS products your business uses. Are those vendors protecting your data securely? A data breach at a key vendor can leave your internal or customer data exposed or even shut down some of your core systems. In some industries like financial services, these types of data breaches are actually the most common kind, and the consequences can be just as severe as if your own systems were breached. AI and emerging technology risk AI risks like shadow AI , overspending on compute , bias, output quality and data privacy all flare up for businesses moving to integrate AI more deeply into daily operations. These risks can affect everything from your monthly AI bill to the quality of information that flows up to decision-makers to your reputation with customers. Most can be managed with careful governance, but many businesses are unaware of just how critical that is. Cybersecurity risk Cybersecurity is the technology risk area most business leaders already understand. That familiarity doesn’t make it any less vital, as a successful breach can cause financial, operational and reputational harm. AI has made it easier than ever to launch a cyberattack, and nation-state actors are increasingly pursuing attacks of their own as well, making managing cyber risk an essential pillar of any enterprise risk strategy. Competition risk Finally, technology is a major competitive edge for businesses that deploy it most effectively. Because technology changes so quickly, leaders need to constantly assess how their industries are using the latest tools, or risk getting left behind. However, you must also balance innovation with enough discipline to avoid investing in new technology simply because it’s new, as that can quickly lead to poor ROI and failed projects. A guide to managing technology risks Effectively managing your technology risks can help your business both avoid bad outcomes and actively generate good ones. Key steps to doing this include: 1. Align technology initiatives with business objectives Only onboard new technologies to solve specific business problems or create defined opportunities. Your organization has goals: Technology should be a tool to reach those goals rather than just for the sake of implementing something cool. 2. Assess technology risks before implementation Before you start using a new technology, assess the risks. For example, consider new risk exposures created by ERP implementation, cloud migration or AI tool as part of your overall process for determining whether to move in that direction. If you’ve already begun a digital transformation without considering risk, do a risk evaluation for your existing digital systems as well. Also, do your due diligence on any third-party vendors you already or plan to work with: Review each vendor’s SOC report (if a vendor doesn’t have one, run) and make sure it reflects an adequate and comprehensive SOC audit . 3. Strengthen governance and accountability Effective governance is a pillar of managing technology risk. To establish or strengthen governance , set up an ongoing governance committee (with C-suite presence or active sponsorship) that will evaluate technology risks, create policies and controls and then train team members to follow them. 4. Conduct ongoing monitoring As you implement new tech tools, conduct ongoing monitoring of technology use. This is both a governance effort to watch for risks and an opportunity to assess whether those tools are delivering ROI. 5. Continuously review and adapt Continuously review your overall technology risk strategy. Consider what you’re learning from your monitoring efforts, as well as how technologies are evolving and the nature of new risks and opportunities. Popular technology risk management frameworks to follow A technology risk management framework is a pre-existing, structured approach to managing IT risk. Often created by institutions, trade associations or international organizations, risk frameworks give you a guidebook for how to assess, mitigate and monitor risk. This is simpler and more effective than creating your own process from scratch. Two popular risk management frameworks you should be aware of include: NIST RMF: Perhaps the biggest technology risk framework, NIST RMF (National Institute of Standards and Technology Risk Management Framework) provides a seven-step process that helps organizations mitigate security, privacy and supply chain risks. COBIT: Another major technology risk framework, COBIT (Control Objectives for Information and Related Technologies), defines a series of processes to manage IT-related risks. If your industry uses a particular technology framework, adopt that one. Otherwise, any established framework is likely a good choice, as most are decades old and heavily vetted. However, don’t mix and match; pick one framework and stick with it rather than trying to pull together elements from several different frameworks. Technology risk management helps businesses modernize with confidence You need up-to-date technology to stay relevant as a business. But how much additional risk do you want to take on as you modernize? Thinking about technology risks strategically, and as part of an overall enterprise risk management strategy , can help dramatically lower your risk exposure. In practical terms, this means lower odds of financial, operational and reputational damages related to technology risks — like a bad data breach that slows down your operations and drives away customers. Embracing an active risk management strategy also helps you learn more about how your business functions, as you work to understand your processes and install stronger controls. This can deliver new efficiencies and process improvements. And finally, consider that managing your technology risk should also boost your confidence in the quality of information that’s coming from inside your organization. You can feel assured that your financial, operational and customer data is accurate, delivering a foundation for smarter business decisions. Read more Enterprise risk management strategy: A short guide for CEOs Shadow AI: How organizations can avoid this growing risk Cybersecurity risk management: A guide for business leaders
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AI risk management: How to adopt AI securely
Your organization is implementing AI. But are you doing it securely, and in a way that doesn’t expose you to unnecessary, avoidable risk? If your answer isn’t a confident yes, then you will almost certainly benefit from implementing an AI risk management strategy. Managing AI risk helps businesses control costs, avoid massive data privacy issues, mitigate shadow AI and use AI tools more effectively. Keep reading to learn more about what AI risk management is, why it matters and put a risk strategy into place. What is AI risk management? AI risk management involves implementing policies, governance and controls to limit your organization’s AI risk exposure. The purpose is to become more secure, boost your AI readiness, manage costs and avoid financial, reputational and operational damages. Although it is a pillar of enterprise risk management and overlaps with other risk areas like cybersecurity, technology or operational risk, AI risk management is different because AI itself is evolving so fast. Nothing about AI is static, which means what is true today may be radically different tomorrow. Traditional risk management principles like vendor management, data security and thoughtful governance are as useful with AI as in other areas. But business leaders must also face new complexities. For example, just because AI always gives you an answer doesn’t mean you can trust it is the right one, as AI models with access to bad data will use that to produce their outputs. Think of AI risk management as a fundamentally new area of risk — one that will only grow more important as AI becomes more deeply integrated into businesses. What is the difference between AI risk management and AI governance? AI risk management is a high-level, strategic effort that fits into your overall enterprise risk management strategy. Governance is the day-to-day process of how you turn an AI risk management strategy into action and results. Your governance committee will take your strategic AI risk management goals and implement them by creating policies, establishing controls and providing training for your team. Why AI risk management matters for business leaders Using AI without first setting up guardrails like governance exposes your organization to major new risks. Without an AI risk management strategy in place, you face financial, operational, reputational or regulatory costs that interfere with the overall success of your business. Financial risk exposure: Ungoverned AI use can create direct financial risks. These can include investing in ineffective AI tools, overspending on AI credits, regulatory fines and other costs associated with putting time and capital into implementing a technology without understanding how to use it correctly. Operational risk exposure: AI can dramatically speed up some aspects of your business — but only if you implement it correctly. Haphazard AI use may not only slow you down but also actively deliver bad information to decision-makers or customers. Reputational risk exposure: Likewise, behaviors like sharing private customer data with the wrong AI model could provide that data to other users, creating major reputational risks. Your organization’s reputation can also falter if you expose customers to inaccurate AI-generated outputs. Regulatory risk exposure: Regulators are still wrapping their heads around AI, but any use that violates applicable rules or guidance could result in fines or corrective action. However, also consider that AI risk management leads to business wins. Identifying AI risks, establishing governance and training your team on AI use are all openings to make AI a stronger organizational asset as well as mitigate risk exposure. In other words, promoting responsible AI use not only helps you avoid trouble but create opportunity. Common AI risks organizations face Any organization that implements AI without a risk management strategy or governance faces certain risks. Key risk areas include data privacy, security, bias, hallucinations, regulatory challenges, third-party vendors and shadow AI. Data privacy and security If your AI tools aren’t containerized (which means non-public, enterprise models), then any data you share will likely end up as part of that model’s available resources for learning and insight, making it accessible to users outside of your organization. If your team doesn’t know to do this, then you may be exposed to massive (and potentially hugely expensive) data privacy issues. AI is also creating new cybersecurity threats. LLMs make it simpler to conduct phishing scams, allowing bad actors to quickly generate thousands of believable phishing emails to flood your organization and look for cracks. And AI also raises new access control issues. If you don’t know who has access to your AI tools or how that access is secured, you could be exposed to unauthorized use. AI bias and model reliability AI answers depend on the data it has available, which creates major bias and reliability risks. A model given inaccurate or incomplete data will create outputs based on that data. This can lead to obviously wrong answers. But it can also create more subtle biases, like a lender that uses an AI tool with access to a dataset containing only affluent borrowers, then asks that tool to help make lending decisions on less affluent ones. The AI may recommend rejecting those borrowers simply because they fall outside the parameters of its available data. Hallucinations Hallucinations — where an AI model simply makes up an answer that isn’t true — are a credible risk. However, the risk level depends heavily on the quality of your data, with models having access to relevant, high-quality data less likely to hallucinate when answering questions related to that data. Prompting also plays a role here, with more careful, accurate prompts typically producing fewer hallucinated responses. Regulatory and compliance risk AI regulations are only just getting off the ground. Key provisions of the European Union’s major regulatory effort, the Artificial Intelligence Act, have only recently taken effect, while similar efforts in the U.S. remain in the planning stages. But businesses in regulated industries need to be careful here. The E.U. law will likely serve as a template for California and other U.S. states to establish AI oversight on a state level sooner rather than later, with a federal bill almost certain to follow. This will be a lot to keep up with, and it’s only going to get more complicated. AI is also shifting certain compliance frameworks. ISO and HITRUST have both added AI standards to their requirements; SOC and other frameworks will likely soon do the same. New AI HIPAA rules are a matter of when, not if. Shadow AI and unauthorized AI use Shadow AI occurs when your employees use unauthorized AI tools at work . This is happening in virtually every organization that doesn’t have strict AI governance in place, creating major risks around data privacy, as shadow AI can easily lead to private data getting shared with public AI models. Shadow AI also exposes you to uncertainty around compute spending, regulatory issues and inaccurate information flowing up to decision-makers. Third-party AI vendor risk As with traditional SaaS products, AI can also expose you to third-party vendor risk. Any data you share with an AI tool — even an enterprise model that’s set up to protect your private data — will likely end up stored in a vendor’s systems, leaving it at risk of exposure during a data breach. You also have to know whether your vendors are meeting regulatory and compliance requirements for your industry, whether the vendor takes appropriate security measures and if its products are at particular risk for bias or hallucinations. Overspending on AI compute Finally, be aware that AI companies like Microsoft, OpenAI and Anthropic have begun raising prices or shifting to new token-based pricing models that charge for actual usage rather than a flat monthly fee. If you don’t know exactly which models your team is using, their pricing structures and how much compute your team is likely to use, you can inadvertently spend more on AI use than you planned to — sometimes much more. Assess your organization’s AI readiness and risks Assessing your organization’s AI readiness and risk levels is a key first step to implementing an effective AI risk management strategy. You can break your assessment down into a handful of buckets: Governance: Do you have an official policy on acceptable AI use ? If not, you need one. Also consider vendor onboarding, as you need a process in place to evaluate and onboard any new approved AI tools. Creating an AI ideas center where employees can bring AI ideas or request new AI tools is a good step too. Business use cases: You should use AI to solve specific problems within your business, not because it’s cool. Creating a roster of business use case ideas for how you should implement AI will help you avoid wasting money and get better results . If you’re unclear on your most valuable use cases, engage an advisor to help you find them. Data security: You need to know where your data is. This could be as simple as moving your data to a central location, like a cloud server, but for more mature organizations, it typically means creating a data lakehouse . Key risks: Evaluate your key risks, like data privacy, shadow AI use, regulations, bias and third-party vendors to understand where you need to implement stronger governance and more effective controls. You may benefit from working with a risk advisor here, as an advisor can help you prioritize risks based on likelihood and impact and then help you mitigate the most important ones. Build an AI governance framework Establishing an effective governance framework is essential to managing your AI risk and avoiding unnecessary costs. AI governance involves creating a governance committee to develop policies and structures that cover areas like customer data privacy, reliability, vendor management, compliance and fairness. To do this, your committee can review sample AI policies and then use those to write one that fits your specific business needs. A governance committee should include C-suite input, typically from your CFO, COO and/or CIO, as well as other stakeholders and individuals who will champion AI use inside your organization. Implement AI controls to reduce business risk Governance itself is the most critical AI control and should be an ongoing effort. Once you have a governance structure in place, you can implement additional controls to reduce your AI risks. You can’t control everything your employees do, but you can establish some measure of institutional control to prevent employees from sharing data with public AI models or granting systems access to unauthorized AI tools. Effective controls include: Ongoing governance Regular AI training for employees Processes for suggesting, assessing and adding new AI tools Regular policy reviews and updates Tracking AI spending Evaluating ROI on specific AI tools Monitoring AI use Think of a three-legged stool here: AI policies to guide use, an AI governance committee to enforce and update them and AI monitoring to track how that work is actually going. Conduct an AI risk assessment before deployment Never invest in a new AI tool without conducting a risk assessment. Before you spend money or share your data with an AI model, you need to understand what it does, how it uses data and whether it fits into your business objectives. Doing a risk assessment can help you avoid security and data privacy problems. As with any new technology, an AI tool should also serve a clear, specific business objective, so taking a measured pause to consider risks is also an opportunity to evaluate if a particular AI tool actually makes sense from an ROI perspective . AI risk management best practices Implementing AI risk management best practices can help your business manage your exposure to AI risks. Beyond establishing governance and a clear AI use policy, here a several specific practices to consider: Maintain a list of authorized AI tools: You should feel confident about how these tools use your data and have an agreement with their vendors that sets clear data protection boundaries that keep your data private. Know how your SaaS platforms are integrating AI: If your ERP or your CRM now includes built-in AI features, learn how those features work and what they do with your data. Ongoing AI use training: People are the biggest risk in cybersecurity and AI is no different. Once you have an AI policy in place, you need to constantly train your team on acceptable AI use to limit your shadow AI risks and keep your policy top of mind. How AI risk management supports enterprise risk management AI risk management is a pillar of a broader enterprise risk management strategy . AI risks create operational, reputational, cybersecurity and financial risks that are not remotely siloed but can bleed into any aspect of your business. For example, consider the story of a major consulting company that published a major report that was full of AI hallucinations. Before catching the errors, the company shared this report widely with customers and its audience, which led to reputational damage once people began to realize the report was not credible. To be most effective, consider AI risks as part of a holistic effort to assess and manage your enterprise risk. You can’t fully separate AI risks from other risk areas, so addressing them through one cohesive strategic push will deliver stronger overall outcomes. Read more AI ROI: How to get more business value from your AI spending AI governance checklist: A basic framework for your business Shadow AI: How business leaders can avoid this growing risk
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Cybersecurity testing best practices: How to validate your cybersecurity program
Leaders at most businesses and organizations understand the value of cybersecurity assessments and implementing a cybersecurity strategy to counter today’s threats. But how do you know if your cyber defenses are working? Conducting regular cybersecurity testing can help validate the efficacy of your cybersecurity program, especially if you follow certain testing best practices. Keep reading to learn more about what that means and how to get started. What is cybersecurity testing? Cybersecurity testing is the process of intentionally looking for weaknesses or vulnerabilities in your systems, network, facilities and equipment that could expose your business to a cyberattack. A cybersecurity testing process may involve doing a vulnerability assessment, conducting penetration testing, auditing your security protocols for compliance with a standard like SOC 2 or HITRUST and determining how to become a more cyber resilient organization . Why cybersecurity testing matters Cybersecurity testing matters because businesses face a complex, dangerous cyber risk environment. Regular testing is a key component of cyber risk management and helps ensure that your business is reducing risk, investing in effective cyber defenses and becoming more resilient against today’s threats. Reduce business risk No cyberdefense is perfect, so it is important to think about cybersecurity in terms of managing risk. Testing helps find security gaps that need to be addressed, which reduces your exposure to the financial , operational and reputational risks that go along with a cyber breach. Improve cyber resilience A cyber-resilient organization is one that can recover from a cyberattack more quickly and with less operational disruption. Cybersecurity testing helps prepare your team to respond to an actual incident, while also uncovering potential weak spots like a lack of data backups that could interfere with your incident recovery. Strengthen regulatory compliance Many businesses need to comply with either industry-specific cybersecurity regulatory requirements or a third-party security framework like SOC 2. Undergoing regular testing helps demonstrate compliance in either scenario. Validate security assessments If you’re conducting a strategic cyber risk assessment , testing can help validate the findings that emerge from that process. Testing also provides ongoing security validation to demonstrate whether your current defenses are up to par. Types of cybersecurity testing Cybersecurity testing can involve several types of tests designed to help you understand whether your cyber defenses are effective and find security gaps. These include vulnerability assessments, penetration testing and security audits: Vulnerability assessments: Vulnerability assessments involve a systematic review of potential security weaknesses in your systems. Various types of assessments exist, including host assessments, network and wireless assessments, database assessments and application scans. Automated testing: Automated testing, such as vulnerability scans, can search for known and common vulnerabilities in applications and compare them against a database of vulnerable programs. Penetration testing: Penetration testing, also known as ethical hacking or pen testing, involves authorized attempts to breach system security. This method simulates real-world cyberattacks to evaluate your defenses. These tests offer a deeper assessment than automated tools, focusing on identifying complex or unknown vulnerabilities that may elude standard scans. Security audits: A cybersecurity audit is a formal process conducted by an independent third-party organization. It acts as a checklist to validate an organization’s cybersecurity policies and procedures. Security audits can provide a snapshot of your cybersecurity health and help you identify any gaps that need remediation. Tabletop exercise: A tabletop exercise involves sitting down with your team and running through how you would respond to a cyberattack. This is essentially a dress rehearsal for the real thing and can help speed up your response time and avoid confusion. Cybersecurity testing best practices Successful cybersecurity testing will typically follow several best practices. These include: Focus on proactive risk management Many businesses tackle cybersecurity strictly from a compliance standpoint — or only after an incident occurs. Taking a proactive approach that recognizes the value of preemptively managing cyber risk both reduces the likelihood an attack will occur and helps your business bounce back faster if it does. Don’t rely on automated testing Automated testing, like a virus scan, can be a useful tool to check for certain common threats or vulnerabilities. But relying only on automated testing can leave you exposed to risks it can’t catch, as well as limit your ability to think strategically about cyber risk. Implement regular manual testing Manual testing — like a vulnerability assessment, penetration testing or a security audit — is essential to implementing a mature cybersecurity program to protect your business. But doing a single penetration test once a year isn’t worth much. Instead, conduct regular manual testing exercises to help ensure you are managing the current threat environment appropriately. Work with a third-party cybersecurity tester Your IT team typically wears many hats, only one of which is cybersecurity. By contrast, a third-party cybersecurity advisor does nothing else. Working with a third-party tester during your testing process can deliver significantly more effective outcomes, finding vulnerabilities you might otherwise miss, recommending new cybersecurity controls and helping you adopt a more strategic defense posture. Look at cybersecurity as a process, not an event Cybersecurity isn’t something you do once a year or even once a month. It should be integrated into your everyday business operations, both in the form of regular manual and automated testing and through ongoing training efforts to keep security top of mind for your team. Consider cyber resilience in your testing process When you’re conducting cyber testing, consider not just whether your defenses are adequate, but how well your organization is prepared to respond to and recover from an attack. Strengthening cyber resilience includes testing activities like tabletop exercises, as well as establishing data backups and system redundancies to avoid prolonged disruptions should an attack occur. Steps to building a risk-based cybersecurity testing program Implementing a risk-based cybersecurity strategy that includes regular testing will increase your organization’s cyber readiness. Here are key steps to help you get started: 1. Identify critical assets Identify the systems, networks, facilities, software and equipment you wish to test. This should include anything that’s critical to your operations or to the protection of your data. 2. Define testing objectives Set clear objectives for what you want to test for. Doing this will help ensure your testing stays focused and produces measurable, actionable outcomes. 3. Select the right testing methods Choose the right tool or method to conduct each test. For example, if you want to assess whether your core systems are secure, you would need to do a penetration test where an ethical hacker would attempt to gain access. 4. Prioritize remediation Investing in testing is a waste of resources unless you act on what you learn. Prioritize remediating any critical gaps identified during testing, while considering others in the context of your overall cyber risk management strategy . 5. Measure and improve over time By making testing a regular, ongoing process, you’ll be able to track your improvements over time. This helps you demonstrate your security maturity to stakeholders like your board, investors, customers and regulators while continuing to manage your cybersecurity risk. FAQ about cybersecurity testing Here are key answers to frequently asked questions about cybersecurity testing: How often should organizations perform cybersecurity testing? At a minimum, organizations should conduct annual security testing that includes a vulnerability assessment and penetration testing to validate that assessment. However, high-risk industries or businesses that are rapidly changing should do so more frequently, perhaps on a quarterly basis. What is the difference between a vulnerability assessment and a penetration test? A vulnerability assessment studies your systems, network and other risk points to find potential vulnerabilities. A penetration test involves actively trying to hack your current cyber defenses to test a weak spot or validate a vulnerability uncovered during the assessment. What types of cybersecurity testing should organizations perform? Organizations should perform both manual and automated cybersecurity testing. Automated testing, like a virus scan, is useful, but is no substitute for active manual testing. The latter, which includes activities like a vulnerability assessment, penetration testing or a security audit, can find gaps or vulnerabilities that an automated test can’t as well as recommend stronger controls. How Wipfli can help We conduct cybersecurity testing and advise organizations on managing cybersecurity risk. Let’s talk about how we can help make your organization more secure. Start a conversation. Let’s make your organization safer Read more A cybersecurity risk management guide for business leaders Your business needs a cyber resilience strategy. Here’s why. Cybersecurity risk assessments: How to get started with yours
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