How to Ensure Data Security in Business

The 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 guidance from 𝗗𝗛𝗦/𝗡𝗦𝗔/𝗙𝗕𝗜 outlines best practices for securing data used in AI systems. Federal CISOs should focus on implementing a comprehensive data security framework that aligns with these recommendations. Below are the suggested steps to take, along with a schedule for implementation. 𝗠𝗮𝗷𝗼𝗿 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Establish Governance Framework     - Define AI security policies based on DHS/CISA guidance.     - Assign roles for AI data governance and conduct risk assessments.  2. Enhance Data Integrity     - Track data provenance using cryptographically signed logs.     - Verify AI training and operational data sources.     - Implement quantum-resistant digital signatures for authentication.  3. Secure Storage & Transmission     - Apply AES-256 encryption for data security.     - Ensure compliance with NIST FIPS 140-3 standards.     - Implement Zero Trust architecture for access control.  4. Mitigate Data Poisoning Risks     - Require certification from data providers and audit datasets.     - Deploy anomaly detection to identify adversarial threats.  5. Monitor Data Drift & Security Validation     - Establish automated monitoring systems.     - Conduct ongoing AI risk assessments.     - Implement retraining processes to counter data drift.  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  Phase 1 (Month 1-3): Governance & Risk Assessment   • Define policies, assign roles, and initiate compliance tracking.   Phase 2 (Month 4-6): Secure Infrastructure   • Deploy encryption and access controls.   • Conduct security audits on AI models. Phase 3 (Month 7-9): Active Threat Monitoring • Implement continuous monitoring for AI data integrity.   • Set up automated alerts for security breaches.   Phase 4 (Month 10-12): Ongoing Assessment & Compliance   • Conduct quarterly audits and risk assessments.   • Validate security effectiveness using industry frameworks.  𝗞𝗲𝘆 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀   • Collaboration: Align with Federal AI security teams.   • Training: Conduct AI cybersecurity education.   • Incident Response: Develop breach handling protocols.   • Regulatory Compliance: Adapt security measures to evolving policies.  

Your brand is likely misusing first-party data and violating customer trust. It's not your intention, but it's probably happening. Here are some common issues I've seen: 1.) Scattering customer data in too many locations - email vendors/CRMs - data warehouses - spreadsheets (eek) 2.) Ignoring permission ...or defaulting to "allow everything" 3.) Not rolling off/expiring data no longer necessary - long-gone churned customers - legacy systems - inactive contact lists 4.) Lack of transparency in how the customer data will be used ...vague or complex privacy/consent policies 5.) Giving too many employees access to sensitive/data ...not everyone needs access to PII/PHI info 6.) Low-security storage - employees accessing cust data on personal devices - lack of roles/permissions - lack of logging 7.) Sharing passwords - bypassing MFA/2FA w/ shared logins - passwords in shared Google Docs - sent via email (ugh) Get caught, and you could face: - significant fines (we're talking millions) - a damaged reputation - loss of customer trust But you can fix this. Here's what to do: - Ask customers what data they're okay sharing - Keep customer data in one secure place (CDP/warehouse) - Only collect what you need (data minimization) - Set clear rules for handling data (who/what) - Offer something in return for data (value trade) - Only let employees access what they need for their job - Use strong protection for all sensitive info - Give each person their own login Your customers will trust you more. Your legal team will be happy. ...and bonus, your marketing will work better. What other data mistakes have you seen? Drop a comment. #dataprivacy #security #consent #dataminimization

I froze for a minute when a client asked me “How do I know my data is safe with you?” Not because I didn’t have an answer But because I knew words alone wouldn’t be enough. After all, trust isn’t built with promises. It’s built with systems. Instead of just saying, “Don’t worry, your data is safe,” I did something different. I showed them: 👉 NDAs that legally protected their information 👉 Strict access controls (only essential team members could ) 👉 Encrypted storage and regular security audits 👉 A proactive approach—addressing risks before they became problems Then, I flipped the script. I told them- “You’re not just trusting me, you’re trusting the systems I’ve built to protect you” That changed everything. → Clients didn’t just feel comfortable—they became loyal. → Referrals skyrocketed because trust isn’t something people keep to themselves. → My business became more credible. And the biggest lesson? 👉 Security isn’t just a checkbox. It’s an experience. Most businesses treat data protection as a technical issue. But it’s an emotional one. When clients feel their information is safe, they don’t just stay. They become your biggest advocates. PS: How do you build trust with your clients?

On a near weekly basis, I read about breaches where much of the exfiltrated data was old data that the organization had no real reason to retain. See, e.g., https://lnkd.in/eaX53AWQ and https://lnkd.in/e4pVA6bT. According to IBM's 2023 Cost of a Data Breach Report, breaches cost organizations an average of $165 per record breached. Report at 2. That means that purging 100,000 records of unnecessary data could save you $16.5M in the event of a breach. Here are five tips: 1. PRACTICE DATA MINIMIZATION: Organizations should practice "data minimization." This means only collecting data that you have a good business reason for collecting and purging unneeded data when it is no longer needed. 2. ARCHIEVE DATA OFFLINE: In one recent example, the breached company apparently "ceased operations in December 2022 but, to comply with legal obligations, . . . maintained an archived copy of data previously stored on its computer systems." See https://lnkd.in/e4pVA6bT. To the extent you are only retaining old data is to satisfy regulatory requirements or just "in an abundance of caution," consider storing the data completely offline, so it is less likely to be breached. 3. CONDUCT A DATA MAPPING: These days it is common for data records to be duplicated in many places across an organization. Thus, consider conducting a regular "data mapping" to ensure that you know where all of your sensitive data is located, that you are adequately protecting it, and that you are purging it when appropriate. 4. IMPLEMENT A WRITTEN POLICY: Be sure to document your data retention and destruction policy in a written policy, and train your employees on the policy regularly. Remember to update the policy to reflect the changing realities in your organization. 5. OVERSEE THE DESTRUCTION OF DATA: Finally, when you destroy data, take reasonable steps to ensure that the data is actually being destroyed. One bank was recently fined $60M for failing to properly oversee a vendor responsible for purging personal data from digital devices. See https://lnkd.in/eutKzpU7.

Sensitive data isn't always what many think it is.   Most people presume it’s limited to financial or health data.   Or credit card and social security numbers.   Then privacy laws came along and changed all of that.   Redefining sensitive data with varying definitions across different regulations.   And depending on the law, sensitive data may now include religious beliefs, over-the-counter med purchases, or precise geolocation data.   Different definitions, different requirements under different privacy laws....   And these discrepancies can lead to serious compliance risks and costly liabilities for businesses if data is not handled correctly within each jurisdiction.   It sure is confusing.   Yet, your company can manage sensitive data with these 4 steps:   1. Understand Your Data → Start by conducting a data inventory → Update the data inventory when new vendors, data processing activities, or technologies are introduced → Regularly assess whether current data collection aligns with business needs and legal requirements   2. Implement Privacy by Design Principles → Build privacy into your products or business systems proactively → Make privacy the default setting → Ensure security, transparency, and respect for user privacy   3. Be Proactive About Privacy Impact Assessments (PIAs) → Conduct a PIA to flag risks before new processes or technologies roll out → Meet legal requirements while enhancing efficiency, compliance, documentation, and transparency with governmental and public bodies → PIAs also help businesses address potential issues with cross-border data transfers   4. Take a Close Look at Your Data Retention Policies → Retain data only as long as needed → Document clear policies for how sensitive data will be deleted or anonymized when no longer needed → Address how privacy rights will be managed   Keep in mind: → Sensitive data needs to have a business purpose to be processed. → Sensitive data collection (and its purposes) need to be disclosed in privacy notices. → And some regulations have specific disclosure requirements around this.   🎉 Bonus tip: Align a likely security focused sensitive data policy with your privacy definitions of sensitive data! This is a common miss among companies and then what is sensitive data internally is confusing! Read our blog for more insights on sensitive data and how you can manage it. Link in the comments 👇  

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

SAP Customer Data security when using 3rd party LLM's SAP ensures the security of customer data when using third-party large language models (LLMs) through a combination of robust technical measures, strict data privacy policies, and adherence to ethical guidelines. Here are the key strategies SAP employs: 1️⃣ Data Anonymization ↳ SAP uses data anonymization techniques to protect sensitive information. ↳ The CAP LLM Plugin, for example, leverages SAP HANA Cloud's anonymization capabilities to remove or alter personally identifiable information (PII) from datasets before they are processed by LLMs. ↳ This ensures that individual privacy is maintained while preserving the business context of the data. 2️⃣ No Sharing of Data with Third-Party LLM Providers ↳ SAP's AI ethics policy explicitly states that they do not share customer data with third-party LLM providers for the purpose of training their models. ↳ This ensures that customer data remains secure and confidential within SAP's ecosystem. 3️⃣ Technical and Organizational Measures (TOMs) ↳ SAP constantly improves upon its Technical and Organizational Measures (TOMs) to protect customer data against unauthorized access, changes, or deletions. ↳ These measures include encryption, access controls, and regular security audits to ensure compliance with global data protection laws. 4️⃣ Compliance with Global Data Protection Laws ↳ SAP adheres to various global data protection regulations, such as GDPR, CCPA, and others. ↳ They have implemented a Data Protection Management System (DPMS) to ensure compliance with these laws and to protect the fundamental rights of individuals whose data is processed by SAP. 5️⃣ Ethical AI Development ↳ SAP's AI ethics policy emphasizes the importance of data protection and privacy. They follow the 10 guiding principles of the UNESCO ↳ Recommendation on the Ethics of Artificial Intelligence, which include privacy, human oversight, and transparency. ↳ This ethical framework governs the development and deployment of AI solutions, ensuring that customer data is handled responsibly. 6️⃣ Security Governance and Risk Management ↳ SAP employs a risk-based methodology to support planning, mitigation, and countermeasures against potential threats. ↳ They integrate security into every aspect of their operations, from development to deployment, following industry standards like NIST and ISO. SAP ensures the security of customer data when using third-party LLMs through data anonymization, strict data sharing policies, robust technical measures, compliance with global data protection laws, ethical AI development, and comprehensive security governance. #sap #saptraining #zarantech #AI #LLM #DataSecurity #india #usa #technology Disclaimer: Image generated using AI tool.

🔐 If It’s Not Encrypted, It’s Not Secure! 🚨 Welcome to Day 2 of this week's Cybersecurity Series—today’s focus: Encrypting ALL Sensitive Data. Imagine a thief breaks into your office but finds all your client files locked in a safe with an unbreakable code. That’s encryption in action. Without it, your sensitive data is wide open for hackers to steal, sell, or ransom. The Reality of Unencrypted Data 🚫 Ransomware Attacks – Hackers steal & encrypt your data, demanding $$$ to restore it 🚫 Man-in-the-Middle Attacks – Cybercriminals intercept emails, contracts, and payment details 🚫 Lost or Stolen Devices – Laptops and USB drives with unencrypted files = massive liability Law firms, accounting firms, and businesses handling PII must treat encryption as a non-negotiable. How to Encrypt & Protect Your Data: ✅ Encrypt Data in Transit – Emails, file transfers, and communications should be end-to-end encrypted ✅ Encrypt Data at Rest – Secure client files, backups, and databases with strong encryption algorithms ✅ Use Encrypted Cloud Storage – Ensure your cloud provider offers default encryption for stored data ✅ Protect Devices with Full-Disk Encryption – Lost devices shouldn’t be a security risk Why It Matters: A business recently suffered a major breach when unencrypted client files were stolen during a cyberattack. Because the data wasn’t protected, they faced lawsuits, compliance fines, and a massive loss of trust. 👉 Watch today’s video on the importance of having an encryption policy (and implementing it!) 📩 DM me if you need help securing your firm’s sensitive information. About Me: I’m a retired FBI Special Agent with over 32 years of experience educating, investigating cybercrime, and helping businesses understand the threats targeting them and repelling those threats. Today, I lead Gold Shield Cyber Investigations & Consulting, helping businesses secure data, respond to breaches, and proactively protect their clients. Follow along this week as we cover the 5 essential cybersecurity areas every business must master. Tomorrow’s topic: Incident Response Planning & Tabletop Exercises! #CyberSecurity #DataEncryption #LawFirmSecurity #PrivacyProtection #CyberThreats #EndToEndEncryption #ZeroTrust #DataProtection #knowledgeisprotection

How robust is your organization's data governance framework? Data governance is crucial for ensuring data quality, security, and compliance across any organization. Here are key components that form the backbone of effective data governance: ✔ Data Stewardship: Managing and overseeing data to ensure its quality, consistency, and compliance with policies and regulations. ✔ Quality Management: Monitoring and improving the accuracy, completeness, and timeliness of data, ensuring trustworthiness and reliability. ✔ Data Ownership: Assigning responsibility and accountability for data assets to specific individuals or teams for proper management and governance. ✔ Data Classification: Categorizing data based on sensitivity, value, or risk to implement appropriate security and compliance measures. ✔ Retention & Archiving: Defining and implementing policies for data retention, storage, and disposal based on legal and business requirements. ✔ Privacy Compliance: Ensuring data management practices adhere to privacy laws and regulations like GDPR or CCPA. ✔ Lineage & Provenance: Tracking the flow of data through systems, ensuring accuracy and compliance by understanding data origin and transformations. ✔ Cataloging & Discovery: Creating a searchable repository for an inventory of data assets, facilitating easy access and management. ✔ Risk Management: Identifying, assessing, and mitigating data-related risks such as breaches or regulatory non-compliance. Implementing a comprehensive data governance strategy can significantly enhance an organization's ability to make data-driven decisions while ensuring compliance and reducing risks. Credits: Deepak Bhardwaj