Technology Articles

Article / Updated 03-03-2025

In this article you will learn: The necessity of discovering and classifying all sensitive data Challenges of modern DSPM tools Solutions that DSPM tools provide Data is often shared and copied across multiple locations, making your most valuable business asset a security nightmare. It’s in your organization’s best interests to keep data as secure as possible, away from prying eyes, no matter where it resides. Doing so requires a structured and strong data security program backed by a powerful data security posture management (DSPM) solution. Modern DSPM is the next generation of data security technology, designed for the complexities of the hybrid cloud environments by tapping into the power of artificial intelligence (AI) and machine learning (ML) to continuously discover data and automate classification of sensitive information across known and unknown environments. The necessity of discovering and classifying all sensitive data Your sensitive data can’t be fully secure unless you’re able to effectively discover and classify every bit and byte of it, quickly and continually, and at scale, across your entire data footprint. That’s what modern DSPM excels at. Essential visibility — what data your enterprise has, how sensitive it is, and where it resides — is the key to security. Gaps in that visibility will stand in the way of a strong security posture. As data is discovered, modern DSPM classifies it automatically and precisely using AI and ML with advanced large language models. Modern DSPM solutions can discover and classify data types that are specific to your organization without any custom tuning. With data discovered and classified, you can then apply the appropriate security controls for keeping that data secure and keeping your organization in compliance with data protection regulations. Modern DSPM excels in both discovery and classification, which is why it’s critical for your data security program’s success. Challenges of modern DSPM tools Today’s data volumes and advanced technologies makes protecting your data a challenge, meaning legacy tools can’t keep up. You’ll find these problems when using a tool that’s not optimized for these challenges: Partially classified data: Legacy tools are designed with out-of-the-box classifiers, set up to only find what it’s looking for. Modern DSPM solutions find what you might not even know you should be looking for, such as unstructured sensitive documents specific to your organization. Discovery is slow: Data security requires rapid information, but legacy tools can’t keep up the pace. Scanning can take a long time because legacy tools don’t benefit from the AI and ML scanning techniques that a modern DSPM tool provides. Inaccurate classifications: Outdated tools tend to rely mainly on regular expressions and other older techniques to classify data. As a result, you need dedicated resources to manually write rules, tune the tools, and make sure classifications are accurate. Difficult to manage: With legacy tools, lots of agents and dedicated resources are needed to connect to datastores. That requires tons of work, and it is a monumental task to maintain. Solutions that DSPM tools provide DSPM, on the other hand, is made for modern data security challenges: Precision in classification and context: A modern DSPM solution applies AI and ML not only to detect common patterns, such as personal identifiers and financial data, but also to recognize proprietary, unstructured, or business-specific data with high precision. Scale and speed for today’s data environments: Precision alone isn’t enough for data that’s fast-moving and dispersed across SaaS, IaaS, and on-prem systems. Speed and scalability are essential. Modern DSPM tools operate agentlessly, scanning datastores quickly without disrupting business operations, providing rapid results, even across vast ecosystems. Actionable insights for remediation: Detecting risks is only the beginning. A powerful DSPM solution goes beyond visibility, offering actionable insights to address identified risks. Modern DSPM is the core that ties everything together and helps ensure the confidentiality, integrity, and availability of your data. You really can’t run a modern data security program without it. To learn more about modern DSPM solutions that support your business needs, download Modern DSPM For Dummies, Cyera Special Edition.

Article / Updated 02-20-2025

Artificial intelligence (AI) offers a lot of promise to companies, but deploying AI can be complex with many considerations and pitfalls. Data is the necessary asset to make AI work, and your organization is probably swimming in it. But how well-managed is your data? The answer hinges on whether your company will be successful in its AI efforts. Data management is the behind-the scenes workhorse that makes AI work. A robust management program allows data to be ingested from everywhere it needs to be, cleaned and transformed to enable AI model training, made easily available to users, and meticulously governed to ensure security, privacy, and compliance. In this article, we’ll explore several ways a data management platform can help with your AI efforts. Although these applications may differ, effective data management is always the necessary first step on which these solutions are built. Proprietary AI As your business scales, the number of receipts, invoices, contracts, and other printed documents scale, too. And when all those documents aren’t digitized, think of the number of hours it will take an employee to catalog it. It’s possible to use AI to automate this process. A proprietary engine scans and processes documents, extracts meaning from them, and outputs the data in a format that’s handy for reports, dashboards, and business intelligence apps. Some benefits of using proprietary AI to scan and process documents are: It can translate multiple languages. A large language model (LLM) can be trained to make sense of any specific document formats that your company may have. Accurate data helps with decision-making. Data can be extracted from third-party platforms, enrich it, validate it, and output it to dashboards accessible throughout the company. Identify issues with customers earlier. Algorithms aggregate and analyze user data, spotlighting any issues with customers early on to prevent customer churn. Or to spotlight when a loyal customer is ready to grow with your company. Retrieval augmented generation Things that work well in a controlled environment with a carefully curated data sample don’t always work in a real-world environment. One such situation is with retrieval augmented generation (RAG), the engine that LLMs rely on to give accurate facts. But if RAG is relying on legacy data that wasn’t prepared adequately, your AI solution is going to underperform. A data management program makes sure the basic, but vitally important, tasks are covered — data is cleaned, engineered, structured, and complete. Some tasks it can do are: Implement meta-intent branching for handling different types of queries. Develop verified quotes and see-it-in-source features for transparency. Monitor and balance token consumption. Improve data quality through semantic data scrubbing. Research and development Traditional research and development methods can be time consuming and expensive. Applying AI to the process can help reduce the cost and release products to the market faster. Reliable products help retain customers, boost the company’s reputation, and grow profit margin. High-quality data is needed to make it all work. A data management program can help with the following tasks: Automate manual processes. Automating helps to lower errors and inefficiencies, and accelerates quantitative research by navigating unstructured data. Business decisions get made faster. Verify and vet output. One system can generate formulas or prototypes for new products; a secondary one can automatically evaluate, compare, and check them for compatibility and other parameters. Enhance new network implementations and diagnostics. AI can create potential scenarios for the design and deployment of new systems. It can create hypotheses to pinpoint problems and suggest solutions. About the Book Wiley has recently published AI Data Management For Dummies, Keboola Special Edition. It includes insights from Snowflake and Capgemini that will help your organization integrate best practices and advanced technologies into your data strategy, what the future of AI development looks like, and more use cases. Download AI Data Management For Dummies, Keboola Special Edition by Andy Mott, Dan O’Riordan, and Rithesh Makkena to open the door to AI success.

Cheat Sheet / Updated 02-19-2025

Before you start using Instagram to promote your business, you may want to learn the lingo that Instagrammers use. Instagram doesn't like accounts that act spammy or over-engage in certain behaviors, so you need to become familiar with a number of restrictions as well as the appropriate image or video size to showcase your products and services. When you follow other Instagram profiles, you can share posts and even entire profiles in a direct message to another Instagram user.

Cheat Sheet / Updated 02-19-2025

Whether you're drafting text in Word, designing presentations in PowerPoint, analyzing data in Excel, or managing your inbox in Outlook, Microsoft Copilot can assist you every step of the way. With this cheat sheet, you'll have a handy reference to quickly understand how Copilot can enhance your productivity and streamline your tasks across various Microsoft 365 programs.

Cheat Sheet / Updated 01-13-2025

This cheat sheet gives you a rundown of some of the most useful features and apps so you can find what you need to customize your iPhone’s behavior. See how to get the most out of Siri (your iPhone’s virtual assistant), review some favorite apps for news and weather, and discover several of the most popular apps for multimedia.

Cheat Sheet / Updated 01-13-2025

So you’re using a Mac running macOS Sequoia? Good move! This Cheat Sheet warns you about six moves to avoid at all costs, gives you a handy reference of keyboard shortcuts that can save you time, teaches you how to navigate the Save As dialog using the Tab key, explains a straightforward protocol for backups, and tells you how to burn CDs from the Music app.

Cheat Sheet / Updated 01-05-2025

Six years after saying Windows 10 was the “last” version of Windows, Microsoft released Windows 11 on October 5, 2021. Although some people say it’s just Windows 10 with a new coat of paint, Windows 11 adds a few new features, removes some old ones, and changes the look and feel of Windows in some subtle ways. These tips help you work with the latest edition of Windows, Windows 11.

Article / Updated 12-09-2024

As you delve deeper into the realm of prompt engineering, you find out that the effectiveness of a prompt can vary greatly depending on the application. Whether you’re using AI for creative writing, data analysis, customer service, or any other specific use, the prompts you use need to be tailored to fit the task at hand. The art in prompt engineering is matching your form of communication to the nature of the task. If you succeed, you’ll unlock the vast potential of AI. For instance, when engaging with AI for creative writing, your prompts should be open-ended and imaginative, encouraging the AI to generate original and diverse ideas. A prompt like “Write a story about a lost civilization discovered by a group of teenagers” sets the stage for a creative narrative. In contrast, data analysis requires prompts that are precise and data-driven. Here, you might need to guide the AI with specific instructions or questions, such as “Analyze the sales data from the last quarter and identify the top-performing products.” You may need to include that data in the prompt if it isn’t already loaded into the training data, retrieval-augmented generation (RAG), system or custom messages, or a specialized GPT. In any case, this type of prompt helps the AI focus on the exact task, ensuring that the output is relevant and actionable. The key to designing effective prompts lies in understanding the domain you’re addressing. Each field has its own set of terminologies, expectations, and objectives. For example, legal prompts require a different structure and language than those used in entertainment or education. It’s essential to incorporate domain-specific knowledge into your prompts to guide the AI in generating the desired output. Following are some examples across various industries that illustrate how prompts can be tailored for domain-specific applications: Legal domain: In the legal industry, precision and formality are paramount. Prompts must be crafted to reflect the meticulous nature of legal language and reasoning. For instance, a prompt for contract analysis might be, “Identify and summarize the obligations and rights of each party as per the contract clauses outlined in Section 2.3 and 4.1.” This prompt is structured to direct the AI to focus on specific sections, reflecting the detailed-oriented nature of legal work. Healthcare domain: In healthcare, prompts must be sensitive to medical terminology and patient privacy. A prompt for medical diagnosis might be, “Given the following anonymized patient symptoms and test results, what are the potential differential diagnoses?” This prompt respects patient confidentiality while leveraging the AI’s capability to process medical data. Education domain: Educational prompts often aim to engage and instruct. A teacher might use a prompt like, “Create a lesson plan that introduces the concept of photosynthesis to 5th graders using interactive activities.” This prompt is designed to generate educational content that is age-appropriate and engaging. Finance domain: In finance, prompts need to be data-driven and analytical. A financial analyst might use a prompt such as, “Analyze the historical price data of XYZ stock over the past year and predict the trend for the next quarter based on the moving average and standard deviation.” This prompt asks the AI to apply specific financial models to real-world data. Marketing domain: Marketing prompts often focus on creativity and audience engagement. A marketing professional could use a prompt like, “Generate a list of catchy headlines for our new eco-friendly product line that will appeal to environmentally conscious consumers.” This prompt encourages the AI to produce creative content that resonates with a target demographic. Software development domain: In software development, prompts can be technical and require understanding of coding languages. A prompt might be, “Debug the following Python code snippet and suggest optimizations for increasing its efficiency.” This prompt is technical, directing the AI to engage with code directly. Customer service domain: For customer service, prompts should be empathetic and solution oriented. A prompt could be, “Draft a response to a customer complaint about a delayed shipment, ensuring to express understanding and offer a compensatory solution.” This prompt guides the AI to handle a delicate situation with care. By understanding the unique requirements and language of each domain, you can craft prompts to effectively guide AI in producing the desired outcomes. It’s not just about giving commands; it’s about framing them in a way that aligns with the goals, terms, and practices of the industry in question. As AI continues to evolve, the ability to engineer precise and effective prompts becomes an increasingly valuable skill across all sectors. 15 tips and tricks for better AI prompting Although GenAI may seem like magic, it takes knowledge and practice to write effective prompts that will generate the content you’re looking for. The following list provides some insider tips and tricks to help you optimize your prompts to get the most out of your interactions with GenAI tools: Know your goal. Decide what you want from the AI — like a simple how-to or a bunch of ideas — before you start asking. Get specific. The clearer you are, the better the AI can help. Ask “How do I bake a beginner's chocolate cake?” instead of just “How do I make a cake?” Keep it simple. Use easy language unless you’re in a special field like law or medicine where using the right terms is necessary. Add context. Give some background if it's a special topic, like tips for small businesses on social media. Play pretend. Tell the AI to act like someone, like a fitness coach, to get answers that fit that role. Try again. If the first answer isn't great, change your question a bit and ask again. Show examples. If you want something creative, show the AI an example to follow, like asking for a poem like one by Robert Frost. Don't overwhelm. Keep your question focused. If it's too packed with info, it gets messy. Mix it up. Try asking in different ways, like with a question or a command, to see what works best. Embrace the multimodal functionality. Multimodal functionality means that the GenAI model you’re working with can accept more than one kind of prompt input. Typically, that means it can accept both text and images in the input. Understand the model’s limitations. GenAI is not infallible and can still produce errors or “hallucinate” responses. Always approach the AI’s output with a critical eye and use it as a starting point rather than the final word on any subject. Leverage the enhanced problem-solving abilities. GenAI’s enhanced problem-solving skills mean that you can tackle more complex prompts. Use this to your advantage when crafting prompts that require a deep dive into a topic. Keep prompts aligned with AI training. For example, remember that GPT-4, like its predecessors, is trained on a vast dataset up to a certain point in time (April 2023 at the time of this writing). It doesn’t know about anything that happened after that date. If you need to reference more recent events or data, provide that context within your prompt. Experiment with different prompt lengths. Short prompts can be useful for quick answers, while longer, more detailed prompts can provide more context and yield more comprehensive responses. Incorporate feedback loops. After receiving a response from your GenAI application, assess its quality and relevance. If it hit — or is close to — the mark, click on the thumbs-up icon. If it’s not quite what you were looking for, provide feedback in your next prompt by clicking on the thumbs-down icon. This iterative process can help refine the AI’s understanding of your requirements and improve the quality of future responses. By keeping these tips in mind and staying informed about the latest developments in the capabilities of various GenAI models and applications, you’ll be able to craft prompts that are not only effective but also responsible and aligned with the AI’s strengths and limitations. How to use prompts to fine-tune the AI model The point of prompt engineering is to carefully compose a prompt that can shape the AI’s learning curve and fine-tune its responses to perfection. In this section, you dive into the art of using prompts to refine the GenAI model, ensuring that it delivers the most accurate and helpful answers possible. In other words, you discover how to use prompts to also teach the model to perform better for you over time. Here are some specific tactics: When you talk to the AI and it gives you answers, tell it if you liked the answer or not. Do this by clicking the thumbs up or thumbs down, or the + or – icons above or below the output. The model will learn how to respond better to you and your prompts over time if you do this consistently. If the AI gives you a weird answer, there's a “do-over” button you can press. It's like asking your friend to explain something again if you didn't get it the first time. Look for “Regenerate Response'’ or some similar wording (term varies among models) near the output. Click on that and you’ll instantly get the AI’s second try! Think of different ways to ask the AI the same or related questions. It's like using magic words to get the best answers. If you're really good at it, you can make a list of prompts that others can use to ask good questions too. Prompt libraries are very helpful to all. It’s smart to look at prompt libraries for ideas when you’re stumped on how or what to prompt. Share your successful prompts. If you find a super good way to ask something, you can share it online (at sites like GitHub) with other prompt engineers and use prompts others have shared there too. Instead of teaching the AI everything from scratch (retraining the model), you can teach it a few more new things through your prompting. Just ask it in different ways to do new things. Over time, it will learn to expand its computations. And with some models, what it learns from your prompts will be stored in its memory. This will improve the outputs it gives you too! Redirect AI biases. If the AI says something that seems mean or unfair, rate it a thumbs down and state why the response was unacceptable in your next prompt. Also, change the way you ask questions going forward to redirect the model away from this tendency. Be transparent and accountable when you work with AI. Tell people why you're asking the AI certain questions and what you hope to get from it. If something goes wrong, try to make it right. It's like being honest about why you borrowed your friend's toy and fixing it if it breaks. Keep learning. The AI world changes a lot, and often. Keep up with new models, features, and tactics, talk to others, and always try to get better at making the AI do increasingly more difficult things. The more you help GenAI learn, the better it gets at helping you! What to do when AI goes wrong When you engage with AI through your prompts, be aware of common pitfalls that can lead to biased or undesirable outcomes. Following are some strategies to avoid these pitfalls, ensuring that your interactions with AI are both effective and ethically sound. Recognize and mitigate biases. Biases in AI can stem from the data it was trained on or the way prompts are structured. For instance, a healthcare algorithm in the United States inadvertently favored white patients over people of color because it used healthcare cost history as a proxy for health needs, which correlated with race. To avoid such biases, carefully consider the variables and language used in your prompts. Ensure they do not inadvertently favor one group over another or perpetuate stereotypes. Question assumptions. Wrong or flawed assumptions can lead to misguided AI behavior. For example, Amazon’s hiring algorithm developed a bias against women because it was trained on resumes predominantly submitted by men. Regularly review the assumptions behind your prompts and be open to challenging and revising them as needed. Avoid overgeneralization. AI can make sweeping generalizations based on limited data. To prevent this, provide diverse and representative examples in your prompts. This helps the AI understand the nuances and variations within the data, leading to more accurate and fair outcomes. Keep your purpose in sight. Losing sight of the purpose of your interaction with AI can result in irrelevant or unhelpful responses. Always align your prompts with the intended goal and avoid being swayed by the AI’s responses into a direction that deviates from your original objective. Diversify information sources. Relying on too narrow a set of information can skew AI responses. Ensure that the data and examples you provide cover a broad spectrum of scenarios and perspectives. This helps the AI develop a well-rounded understanding of the task at hand. For example, if the AI is trained to find causes of helicopter crashes and the only dataset the AI has is of events when helicopters crash, it will deduce that all helicopters crash which in turn will render skewed outputs that could be costly or even dangerous. Add data on flights or events when helicopters did not crash, and you’ll get better outputs because the model has more diverse and more complete information to analyze. Encourage open debate. AI can sometimes truncate debate by providing authoritative-sounding answers. Encourage open-ended prompts that allow for multiple viewpoints and be critical of the AI’s responses. This fosters a more thoughtful and comprehensive exploration of the topic. Be wary of consensus. Defaulting to consensus can be tempting, especially when AI confirms our existing beliefs. However, it’s important to challenge the AI and yourself by considering alternative viewpoints and counterarguments. This helps in uncovering potential blind spots and biases. Check your work. Always review the AI’s responses for accuracy and bias. As with the healthcare algorithm that skewed resources toward white patients, unintended consequences can arise from seemingly neutral variables. Rigorous checks and balances are necessary to ensure the AI’s outputs align with ethical standards.

Article / Updated 11-20-2024

Zero Risk application security provides comprehensive protection against potential threats, ensuring the integrity, confidentiality, and availability of your vital data and systems. This approach integrates automated risk analysis, stringent access provisioning controls, and continuous monitoring to prevent unauthorized access and vulnerabilities. The goal is to neutralize potential threats before they cause harm, which helps maintain the highest levels of visibility, security, and compliance. Aligning your organization’s Zero Risk strategies with established cybersecurity and regulatory compliance frameworks is a necessity. The goal is to create a robust security posture that not only protects your assets but also ensures you meet the critical standards set by regulatory bodies. Understanding the compliance landscape Before you can align your Zero Risk strategies with compliance frameworks, you must understand the landscape. Two of the most significant regulatory frameworks in this realm are the Sarbanes-Oxley Act (SOX) and the General Data Protection Regulation (GDPR). SOX focuses on financial reporting and corporate governance, while GDPR is concerned with data protection and privacy for individuals within the European Union. The National Institute of Standards and Technology (NIST) also provides a risk management framework that can be leveraged to align with Zero Risk strategies. This framework helps your organization manage and mitigate risks in a comprehensive and repeatable manner, ensuring that cybersecurity remains a dynamic and integral part of your organization’s culture and business processes. To hit the mark on Zero Risk, you also need to mature your cybersecurity model. The Cybersecurity Maturity Model Certification (CMMC) enhances your security practices through five levels of cyber hygiene: Level 1: Basic cyber hygiene: Figure out your current cybersecurity practices and understanding the basics. Level 2: Intermediate cyber hygiene: Document your cybersecurity practices consistency and repeatability. Level 3: Good cyber hygiene: Put proper risk management practices in place to address identified vulnerabilities and threats. Level 4: Proactive cyber hygiene: Routinely and regularly review risks. Continuous monitoring ensures you’re proactive in managing them. Level 5: Advanced and progressive cyber hygiene: Optimize your cybersecurity processes and continuously improving to stay ahead of emerging threats. Focusing on key actions to alignment Aligning Zero Risk with the compliance frameworks involves several key actions: Gap analysis: Conduct a thorough comparison of your current policies, processes, and technologies against the requirements of SOX, GDPR, and the NIST framework. This analysis highlights areas that need improvement and helps prioritize your efforts. Remediation roadmap: Develop a detailed plan (a roadmap of sorts) to address the identified gaps (see the preceding bullet). This plan should prioritize critical areas, allocate resources, and set timelines for completion. Policy communication: Update your policies to align with SOX and GDPR standards, and communicate these changes effectively across your organization. Taking practical measures for compliance To ensure that your Zero Risk strategies are in sync with compliance frameworks, consider the following practical measures: Automating monitoring and reporting: Use identity and application access governance software to automate the monitoring of your systems and the reporting process. This measure not only saves time but also reduces the risk of human error. Regular training: Conduct regular training and awareness programs for employees to ensure that they understand their roles in protecting data and maintaining financial integrity. Continuous monitoring: Implement real-time tracking of all transactions within the application environment. This helps maintain an up-to-date security posture and immediate threat detection. Maintaining compliance and security After you’ve aligned your Zero Risk strategies with compliance frameworks, maintaining that alignment over time is crucial. To stay aligned and ensure ongoing compliance, follow these tips: Perform audits. Conduct regular internal audits to test your financial controls and data security procedures. This action helps identify any compliance drift early on. Stay updated. Keep abreast of changes in compliance frameworks and adjust your policies and procedures accordingly. This means subscribing to regulatory updates, attending relevant seminars and webinars, and participating in industry groups. Regularly revisit your compliance frameworks to ensure they reflect the latest legal and regulatory changes. Document, document, document. Keep an up-to-date record of all data processing activities. This documentation is key to demonstrating compliance during audits. Aligning Zero Risk strategies with cybersecurity and regulatory compliance frameworks is a dynamic process that requires continuous attention and adaptation. By taking a systematic approach to gap analysis, remediation, and ongoing maintenance, organizations can not only enhance their security posture but also ensure they meet the evolving demands of regulatory compliance. This integrated strategy supports sustainable growth and resilience in an increasingly complex digital landscape. Achieving compliance is an accomplishment, but maintaining it is the goal. Falling out of compliance can be costly, so keep reviewing and updating your cybersecurity model to stay ahead of the curve. How Pathlock can help Your organization needs to achieve a Zero Risk application environment, and Pathlock can help. Pathlock’s solutions give you critical tools for planning a proactive defense strategy to continuously monitor and manage risks. To find out more, check out Zero Risk Application Security For Dummies, Pathlock Special Edition. Head to pathlock.com/resource/zero-risk-application-security-for-dummies for your free e-book, and start planning your Zero Risk application security approach.

Cheat Sheet / Updated 11-13-2024

Drone piloting is for fun . . . and profit, if you want to go that route! It can start out as a hobby and become a side hustle or even a full-time job in a particular industry. From giving you tips about buying a drone, to flying it safely, to taking the Part 107 exam, to cranking up a freelance business, this cheat sheet can help you get your drone piloting goals off the ground.