Tag Archives: data

WHAT ARE THE KEY SECURITY MEASURES THAT WILL BE IMPLEMENTED TO PROTECT SENSITIVE CUSTOMER DATA

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We take customer data security extremely seriously. Safeguarding sensitive information and upholding the highest standards of privacy and data protection are fundamental to maintaining customer trust.

Our information security management system has been designed according to the ISO/IEC 27001 international standard for information security. This ensures that information risks are properly identified and addressed through a robust set of security policies, procedures, and controls.

We conduct regular security audits and reviews to identify any gaps or issues. Any non-conformities identified through auditing are documented, assigned ownership, and tracked to completion. This allows us to continually evaluate and improve our security posture over time.

All customer-related data is stored within secure database servers located in ISO/IEC 27017 compliant data centers. The data centers have stringent physical and environmental controls to prevent unauthorized access, damage, or interference. Entry is restricted and continuously monitored with security cameras.

The database servers are deployed in a segmented, multi-tier architecture with firewalls and network access controls separating each tier from one another. Database activity and access is logged for audit and detection purposes. Critical systems and databases are replicated to secondary failover instances in separate availability zones to ensure continuity of operations.

Encryption is implemented throughout to protect data confidentiality. Data transmitted over public networks is encrypted using TLS 1.3. Data stored ‘at rest’ within databases and files is encrypted using AES-256. Cryptographic keys are securely stored androtated regularly per our key management policy.

We perform regular vulnerability scanning of internet-facing applications and network infrastructure using manual and automated tools. Any critical or high-risk vulnerabilities identified are prioritized and remediated immediately according to a defined severity/response matrix.

Access to systems and data is governed through the principle of least privilege – users are only granted the minimal permissions necessary to perform their work. A strong authentication system based on multi-factor authentication is implemented for all access. User accounts are reviewed periodically and deactivated promptly on staff termination.

A centralized identity and access management system provides single sign-on capability while enforcing centralized access controls, approval workflows and automatic provisioning/deprovisioning of accounts and entitlements. Detailed system change, access and activity logs are retained for audit and reviewed for anomalies.

Robust monitoring and threat detection mechanisms are put in place using security information and event management (SIEM) solutions to detect cybersecurity incidents in real-time. Anomalous or malicious activity triggers alerts that are reviewed by our security operations center for an immediate response.

Data loss prevention measures detect and prevent unauthorized transfer of sensitive data onto systems or removable media. Watermarking is used to help identify the source if confidential data is compromised despite protective measures.

Vendor and third party access is tightly controlled and monitored. We conduct security and compliance due diligence on all our service providers. Legally binding agreements obligate them to implement security controls meeting our standards and to notify us immediately of any incidents involving customer data.

All employees undergo regular security awareness training to learn how to identify and avoid social engineering techniques like phishing. Strict policies prohibit connections to unsecured or public Wi-Fi networks, use of removable storage devices or unauthorized SaaS applications. Breaches are subject to disciplinary action.

We conduct simulated cyber attacks and tabletop exercises to evaluate the efficacy of our plans and responses. Lessons learned are used to further improve security controls. An independent external auditor also conducts annual privacy and security assessments to verify ongoing compliance with security and privacy standards.

We are committed to safeguarding customer privacy through stringent controls and will continue to invest in people, processes and technologies to strengthen our defenses against evolving cyber threats. Ensuring the highest standards of security is the priority in maintaining our customers’ trust.

HOW CAN STRICTER SECURITY PRACTICES AND DATA PRIVACY LAWS HELP PREVENT DATA BREACHES AND CYBER ATTACKS?

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Implementing stricter security practices and enacting stronger data privacy laws are two effective approaches that can help curb data breaches and cyber attacks. Together, they create a more robust framework of protections for individuals and organizations.

On the security front, organizations need to make cybersecurity a top priority. This means investing adequately in people, processes, and technologies. Funding should go towards hiring and training expert security personnel who can implement thorough risk assessments, vulnerability management programs, patching routines, access controls, multi-factor authentication, encryption, monitoring solutions, and incident response plans. Regular security awareness training is also crucial for keeping all employees vigilant against social engineering attacks like phishing.

Regular external security audits help ensure compliance to standards and identify gaps before they are exploited. It is also wise for companies to segment their networks to limit the spread of intrusions. They must also carefully vet third-party vendors that handle their data and ensure rigorous oversight of those connections. Critical systems should be properly air-gapped from the internet whenever possible.

Implementing the principle of “least privilege” is important – users and applications should only have the bare minimum permissions required for their roles. Application development best practices like secure coding are a must as well. Companies should responsibly disclose vulnerabilities to give bad actors less opportunity for advanced attacks. Penetration testing can also uncover weaknesses ahead of time.

In addition to technical defenses, human and administrative controls are important. Strong policies around password hygiene, remote working, removable media usage and more set clear behavioral expectations. Compliance is monitored and violations dealt with appropriately. Data handling practices must be governed by compliance to standards like privacy by design. Comprehensive incident response plans ensure rapid containment and remediation in the event of breaches.

On the legal and regulatory front, binding data privacy laws with stiff penalties for non-compliance drive higher security standards across the board. Some key components of an effective privacy law include:

Mandating the implementation of reasonable security measures through compliance frameworks like ISO27001 or NIST CSF. These frameworks provide guidance on international best practices.

Requiring notification of data breaches within a strict timeframe, say 72 hours of discovery. This enables timely response and mitigation.

Compelling removal of legal barriers to information sharing about threats through bodies like CERTs.

Data minimization principles obligating companies to limit collection and retention of personal information. This shrinks the attack surface.

Giving data subjects accessible rights to access, modify, erase their information held by companies. This enables oversight and accountability.

Implementing the principle of data protection by design ensuring privacy is a foremost consideration in system planning.

Empowering data protection authorities with inspection powers, ability to issue fines and audit for compliance. “Teeth” in laws drive better accountability.

Extending coverage beyond just sensitive financial and health data to recognize importance of all personal data in the digital world.

Enacting strong international data transfer controls preventing irresponsible movement of citizen’s information across borders.

Providing unambiguous definitions of personal data, roles and responsibilities to limit loopholes.

Whistleblower protections empowering individuals to flag non-compliance without fear of reprisals.

Strengthening both technical security practices and privacy laws in harmonious tandem is crucial. Legal provisions drive overall policy shift and infrastructure upgrades in the long run. But active security risk management, monitoring and continual improvements remain essential for resilient protection. Comprehensive “security by design” and lifecycle management practices embedded through legislation will go furthest in achieving cyber-safety for people, services and businesses in the digital age.

HOW CAN HOSPITALITY BUSINESSES ENSURE DATA SECURITY AND CUSTOMER PRIVACY WHEN ADOPTING NEW TECHNOLOGIES?

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As hospitality businesses adopt new technologies like online booking platforms, mobile apps, smart lock systems, and IoT devices, they are collecting and storing more customer data than ever before. While these technologies provide many benefits, they also introduce new data security and privacy risks that need to be properly addressed. There are a number of proactive steps businesses can take to ensure customer data remains secure and privacy is respected when introducing new systems.

First, businesses need to inventory all customer data assets and map where data is collected, stored, shared and processed. This data mapping exercise helps identify security and privacy risks and compliance requirements. It is important to understand what type of data is being collected from customers (names, addresses, payment info, travel preferences etc.) and how this data flows through internal IT systems and third party services. Any data that is transferred to external vendors or stored in the cloud also needs to be identified.

Once all customer data assets are mapped, the business should conduct a comprehensive privacy and security risk assessment. This involves identifying potential threats like hacking, data breaches, unauthorized access or disclosure and evaluating the likelihood and impact of such risks materializing. The risk assessment helps prioritize security controls based on risk level. It is also important to identify any legal or regulatory compliance requirements like GDPR in Europe which mandate how customer personal data must be handled.

Strong access controls and authorization protocols need to be established for all systems processing customer data. Role-based access control should be implemented to restrict data access to only authorized personnel on a need-to-know basis. Multi-factor authentication is also recommended for sensitive systems. Next, the principle of “data minimization” should be followed – only collecting the minimum amount of customer data needed to support business functions. Data should also have expiration dates after which it is automatically deleted.

Robust technical security controls also need to implemented based on the risk assessment. This includes measures like data encryption of customer files at rest and in transit, intrusion detection and prevention systems, log monitoring, regular security patching, configuration hardening etc. to prevent data theft or leakage. Web applications should also be rigorously tested for vulnerabilities during development using techniques like penetration testing. Infrastructure security controls ensuring network segmentation, firewall rulesets, etc. must be reviewed periodically as well.

Strict confidentiality and privacy policies governing employee conduct and responsibilities need to be established. Rigorous background checks should be performed for employees handling sensitive data. Ongoing security awareness training is important to educate staff on cyber risks, zero day threats and their role in protecting customer privacy and securing systems. Robust governance measures like access logs, regular vulnerability scanning and audits help verify compliance.

Customers also need transparency into how their data is collected and used via detailed privacy policies. They should be able to access, correct or delete personal data easily as per regulation. Customer privacy preferences like opting out of data sharing with third parties need to be respected. If any data breaches occur, affected customers must be notified promptly as required by law. Adopting a “privacy by design” approach ensures customer needs are prioritized right from the start.

Implementing strong accountability measures through senior management oversight and establishing an incident response plans in case of breaches are equally crucial. Outsourcing certain controls to expert managed security service providers may also help plug capability gaps, especially for small and medium businesses. Customers will continue trusting businesses only if they are convinced robust data stewardship is a top priority alongside innovation. Taking a comprehensive, risk-based approach to security and privacy can help win that trust.

While new technologies offer many opportunities, customer data protection must remain the top concern for any hospitality business. Implementing security controls across people, processes and technologies at each stage of the data lifecycle helps strike the right balance between progress and responsibility. With diligence and care, businesses can harness digital innovations to enhance service and experience, without compromising on customer confidence.

COULD YOU EXPLAIN HOW TO CREATE A DYNAMIC DASHBOARD IN EXCEL FOR DATA VISUALIZATION

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A dynamic dashboard in Excel allows you to visualize changing data in real-time or near real-time to gain insights and track key performance indicators (KPIs). It allows non-technical users to see their constantly updating data in an easy-to-understand format without needing to regularly refresh or update their reports manually. Creating a dynamic Excel dashboard involves the following steps:

Plan your dashboard – The first step is to plan out what type of data you need to display and the key metrics or KPIs you want to track. Determine things like the data sources, the frequency with which the data will update, the visualizations needed, and how the dashboard will be accessed and updated. Sketch out on paper how you want the dashboard to look and operate.

Setup data connections – You’ll need to connect your dashboard workbook to the underlying data sources. For Excel, common data connection types include connecting to other worksheets or workbooks within the same file, connecting to external data stored in text/CSV/XML files, connecting to external databases like SQL Server, and connecting to online data sources through OData web queries. Use things like Excel’s built-in Get Data tools and functions like power query to automatically import and structure your data.

Automate data refreshes – For a true dynamic dashboard, you need the data visualizations to update automatically as the underlying data changes. This is done by setting up scheduled data refreshes using Excel’s Data Refresh tool. you can refresh the queries and pivot tables on a schedule linking to external data. For example, you may want to refresh the data daily at 6 AM to pull in the previous day’s data. You can also trigger refreshes manually.

Design interactive visuals – The dashboard should display your key metrics through various interactive visualizations like charts, gauges, maps, pivot tables and more. You can use Excel’s wide range of built-in chart types as well as more advanced types through add-ins. Ensure the visuals are formatted properly for readability and aesthetics. Add relevant titles, labels, data labels, colors, tooltips etc.

Filter and slice data – Enable users to filter the visuals by parameters to drill-down into subsets of the data. For example, allow filtering a chart by region, product, date range etc. You can add slicers, filters or combo boxes linked to pivot tables/queries for this.

Add KPIs and metrics – KPIs are critical data points that need to be prominently displayed and tracked over time. Use gauge charts, traffic lights, meter charts etc to visualize KPI values against targets. Add relevant background colors, icon graphics and call-outs. Power BI also allows building KPI scorecards from Excel data.

Format for mobile – Consider if dashboard needs to be accessed on mobile screens. Use responsive design principles like auto-fitting charts, larger text, fewer/simpler elements on mobile views. Explore tools like Power BI for reports accessible on any device.

Protect and share – Password protect or restrict access to the file if needed. Publish Power BI dashboards securely online. Share workbook links for read-only external access. This allows distributed teams to monitor metrics remotely.

Test and refine – Thoroughly test all the interactivity, refreshing, formatting on different systems before implementing the dashboard for actual use. Monitor for issues, get feedback and refine design iteratively based on user experience. Consider automation add-ins for enhanced formatting, lay-outing and governance.

Maintain and evolve – As needs change, the dashboard should evolve. Streamline the maintenance processes by version controlling the file, documenting procedures and changes. Train others to extend, refresh or make modifications as required. Monitor dashboard usage and determine if new metrics/visualizations need to be added or obsolete ones removed over time.

This covers creating a robust, dynamic Excel dashboard from planning to implementation to maintenance. Some key advantages are easy creation without coding for business users, familiar Excel interface, interactive data exploration within the sheet itself and mobility across devices. With latest tools in Excel and Power BI, sophisticated dashboards can now be built directly in Excel to drive better business decisions through data. Regular refinement keeps the dashboard aligned to the evolving needs.

WHAT ARE SOME POPULAR PROGRAMMING LANGUAGES USED IN IBM DATA SCIENCE CAPSTONE PROJECTS ON GITHUB

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Python is by far the most commonly used programming language for IBM data science capstone projects on GitHub. Python has become the dominant language for data science due to its rich ecosystem of packages and libraries for data wrangling, analysis, visualization, and machine learning. Key Python data science libraries like Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Keras, and Tensorflow are ubiquitously used across projects. Python’s clear and readable syntax also makes it very approachable for newcomers to data science. Many capstone projects involve analyzing datasets from a variety of domains using Python for tasks like data preprocessing, exploratory data analysis, building predictive models, and creating dashboards and reports to communicate findings.

R is another popular option, especially for more statistics-focused projects. R’s strengths lie in implementing statistical techniques and modeling, and it includes powerful packages like ggplot2, dplyr, and caret that are very useful for data scientists. While Python has gained more wide adoption overall, R still maintains an active user base in fields like healthcare, finance, marketing that involve intensive statistical analysis. Some IBM data science capstones apply R for predictive modeling on tabular datasets or for time series forecasting problems. Data visualization is another common application thanks to R’s graphics capabilities.

JavaScript has increased in usage over the years and is now a viable language choice for front-end data visualization projects. D3.js in particular enables creation of complex, interactive data visualizations and dashboards that can be embedded within web pages or apps. Some capstones take JSON or CSV datasets and implement D3.js to build beautiful, functional visualization products that tell insightful stories through the data. JavaScript’s versatility also allows integration with other languages – projects may preprocess data in Python/R and then render results with D3.js.

SQL (often SQLite) serves an important role for projects involving relational databases. Even if the final analysis is done in Python/R, an initial step usually involves extracting/transforming relevant data from database tables with SQL queries. Healthcare datasets in particular are commonly extracted from SQL databases. SQL knowledge is invaluable for any data scientist working with structured datasets.

Most machine learning engineering capstones will involve some use of frameworks like TensorFlow or PyTorch when building complex deep learning models. These frameworks enable quick experimentation with neural networks on large datasets. Models are trained in Python notebooks but end up deployed using the core TensorFlow/PyTorch libraries. Computer vision and NLP problems especially lend themselves to deep learning techniques.

Java is still prevalent for projects requiring more traditional software engineering skills rather than pure data science. For example, building full-stack web services with backend APIs and database integration. frameworks like Spark and Hadoop see usage as well for working with massive datasets beyond a single machine’s resources. Scala also comes up occasionally for projects leveraging Spark’s capabilities.

While the above languages dominate, a few other options do come up from time to time depending on the specific problem and use case. Languages like C/C++, Go, Swift may be used for performance-critical applications or when interfacing with low-level system functionality. MATLAB finds application in signal processing projects. PHP, Node.js, etc. can be applied for full-stack web/app development. Rust and Haskell provide quality alternatives for systems programming related tasks.

Python serves as the most popular Swiss army knife for general data science work. R maintains a strong following as well, especially in domains requiring advanced statistical modeling. SQL is ubiquitous for working with relational data. JavaScript enables data visualization. Deep learning projects tend to use TensorFlow/PyTorch. Java powers more traditional software projects. The choice often depends on the dataset, goals of analysis, and any specialized technical requirements – but these programming languages cover the vast majority of IBM data science capstone work on GitHub. Mastering one or two from this toolkit ensures data scientists have the tools needed to tackle a wide range of problems.