Project Management Data Security

I have 6 years of experience and work as a Sr. Security Engineer at Google, and I have seen identity and access management scare a lot of junior security engineers. It is one of the most complex topics in cybersecurity and security interviews. It covers a lot of important topics: Authentication, authorization, tokens, sessions, OAuth, SSO, RBAC, service accounts, secrets, you name it. But once you understand these 15 must-know concepts, everything starts to make a lot more sense. 15 IAM concepts every security engineer should know: 1. Authentication Who are you? 2. Authorization What are you allowed to do? 3. Least privilege Give the minimum access needed. Nothing more. 4. RBAC Access based on role, like admin, analyst, viewer. 5. ABAC Access based on attributes like team, region, device, and environment. 6. MFA A password alone is not enough anymore. 7. Session management Login is not the end. Sessions must expire, rotate, and be invalidated. 8. Access tokens Short-lived proof that lets an app call another system. 9. Refresh tokens Used to get new access tokens without logging in again. 10. OAuth 2.0 A delegated access framework. Very common. Very misunderstood. 11. OpenID Connect Identity layer on top of OAuth. This is how login often works. 12. Service accounts Non-human identities used by apps, jobs, and automation. 13. Workload identity A safer way for workloads to get cloud access without static keys. 14. Secret rotation and revocation If a token, key, or secret leaks, you need to kill and replace it fast. 15. Audit logs and access reviews If you cannot see who accessed what, you are already behind. Most security incidents are not caused by “hackers being geniuses.” They happen because identity was weak, access was too broad, tokens lived too long, or no one checked the logs. If you understand IAM well, a lot of security starts to click for you. -- 📢 Follow saed if you enjoyed this post 🔖 Be sure to subscribe to the newsletter: https://lnkd.in/eD7hgbnk 📹 Reach me on https://lnkd.in/eZ9mU5Ka for open DM's

3 Minutes of Downtime. Full Microsegmentation. No Changes Required. Most cybersecurity conversations still assume one thing: You can change the environment. But what happens when you can’t? On the plant floor, there are no easy resets. No patch windows. No tolerance for downtime. We’re talking about: • Legacy machines that can’t be touched • Unmanaged assets • Systems where even 1 hour of downtime can cost millions This is where Byos delivers real-world value. In this clip, we walk through a real deployment: Securing a plant floor with CNC and hydraulic machines running on an existing network, without disruption. No rip-and-replace. No re-architecture. No operational risk. We deployed Byos hardware-enforced microsegmentation in-line with: • Minimal downtime (Less than 3 minutes of downtime per machine) • No changes to existing assets or network • Immediate visibility and control That’s the difference between theory and reality: Because in these environments, security isn’t about adding controls. It’s about enforcing them without breaking what already works. If your environment can’t afford trial and error, your security architecture shouldn’t depend on it. Watch the clip to see how hardware-enforced microsegmentation performs where software can’t. #cybersecurity #HardwareMicrosegmentation #Manufacturing #LegacySystems #SASE #FIPS140-2 #ICS #OT #IoT #BYOS

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.

Your biggest cybersecurity threat might not be your employees — it might be your coffee machine. Everyone’s worried about employees clicking phishing emails… …but who’s worried about the smart thermostat leaking your sensitive data? (You should be.) When we talk about human cyber risk, it’s not just laptops and emails. It’s the people who plug in devices they don’t understand — or don’t think about — that open the backdoor. The truth is: The Internet of Things (IoT) is your weakest (and most ignored) security link. 📺 Smart TVs. 🏅 Fitness trackers. ☕ Coffee machines. 🔔 Video doorbells. 💡 Smart lighting. 🌡️ Even that “harmless” Wi-Fi-enabled fish tank thermometer in your lobby. (Yes, that actually happened to a casino in 2019 where the whole high roller database was exfiltrated through an IoT connected fish tank thermometer. Ouch.) If it connects to the internet, it can connect a threat actor to you. ACTIONABLE TAKEAWAYS: ✔️ Audit your IoT Devices: List everything in your business and home that’s internet-connected. If you don’t track it, you can’t protect it. ✔️ Segregate Networks: Keep IoT devices on a separate Wi-Fi network from business operations and sensitive information. ✔️ Change Default Credentials: Most IoT breaches happen because devices are left on factory settings. Change all passwords — immediately. ✔️ Update Firmware: Your smart devices need updates just like your computer does. Patch regularly or retire them if they’re no longer supported. ✔️ Train Your People: If they’re plugging it in, they’re opening a portal. Awareness matters. Train users to think before they connect. Bottom line: Human risk isn’t just about bad passwords and phishing clicks. It’s about our instinct to trust technology we don’t fully understand. If you employ humans, if you use IoT, you have risk. Manage your humans. Manage your tech. Or someone else will. #HumanRisk #Cybersecurity #IoTSecurity #InsiderThreat #CyberHygiene #Leadership #SecurityAwareness

Personal data is highly sensitive information we entrust to internet companies, and strong regulations require these companies to handle it safely and reliably to meet security, privacy, and compliance standards. In this tech blog, Airbnb’s data science team shares how they built a data classification workflow to establish a unified strategy for identifying and classifying data across all data stores. The workflow is built on three pillars: Catalog, Detection, and Reconciliation. The Catalog pillar focuses on creating a dynamic and accurate system to identify where data resides and organize it into a comprehensive inventory. Detection addresses the question: what data might be considered personal? This step involves a detection engine structured as a pipeline to scan, validate, and control thresholds for surfacing detected results. Finally, Reconciliation ensures accurate classification by involving data owners in a human-in-the-loop process to confirm or refine detected classifications. Given the complexity of the system, the team developed metrics to assess its quality. These metrics—recall, precision, and speed—evaluate how effectively, accurately, and efficiently the classification system operates, ensuring it safeguards personal data over the long term. Additionally, the team shares strategies for governing data classification early in the process, along with best practices for improving workflows. These insights provide a clear understanding of not only the metrics but also actionable ways to enhance classification systems. Highly recommended reading for anyone interested in data governance and security. #datascience #personal #data #governance #classification #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqxuQ29E

Why Identity Access Management Is Critical for Modern Enterprises Identity Access Management (IAM) is the vital part of any robust security architecture - especially as traditional perimeters dissolve in today’s distributed environments. For technical leaders and practitioners, effective IAM isn’t just about authentication. It’s about implementing continuous, granular controls that adapt to organizational change and emerging risk. Key pillars include: User Access Reconciliation: Regular alignment of granted permissions with actual entitlements in critical systems is non-negotiable. Automated and periodic reconciliation detects orphaned accounts and excessive privileges, reducing attack surfaces. Privileged Access Management (PAM): High-risk accounts with broad capabilities must be tightly governed. PAM enforces strict controls such as just-in-time elevation, session monitoring, and audit trails to protect sensitive assets from exploitation. Timely Access Revocation: When users change roles or exit, immediate deprovisioning is crucial. Delays can leave dormant accounts vulnerable to misuse or compromise. Automated workflows ensure access rights are always in sync with current employment status and responsibilities. Principle of Least Privilege: Users should have the minimal access needed to perform their functions - nothing more. This foundational control limits exposure and contains lateral movement in case of breaches. Periodic Role Transition Audits: Role transitions are inevitable. Regular reviews of access entitlements ensure that evolving responsibilities are matched by appropriate authorizations, preventing privilege creep and segregation-of-duty violations. In a zero-trust era, identity is the new perimeter. Mature IAM programs employ multifactor authentication, continuous role audits, and real-time response to changes, providing both agility and security at enterprise scale. #IAM #CyberSecurity #IdentityManagement #PAM #ZeroTrust

90% of data projects fail because of bad data, not bad models. (Learnt it the hard way!) 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐭𝐡𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Everyone talks about fancy algorithms and cutting-edge models. But your analysis is only as good as your data. And most data? It's a mess. Duplicates. Missing values. Inconsistent formats. Different time zones. 𝐓𝐡𝐞 4-𝐬𝐭𝐞𝐩 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐈 𝐮𝐬𝐞 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭: 𝟏. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐚𝐤𝐞 & 𝐀𝐮𝐝𝐢𝐭 → Check schema, completeness, and validity first → Hunt for duplicates and PII data → Visualize missing patterns (they tell a story) → Master this: Your foundation determines everything 𝟐. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 – 𝐅𝐢𝐱 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐄𝐫𝐫𝐨𝐫𝐬 → Standardize labels (yes/Yes/YES → yes) → Merge duplicates the smart way → Fix units and time zones NOW, not later → Pro tip: Document every transformation 𝟑. 𝐈𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧, 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 & 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐞𝐩 → Handle missing data based on business logic → Encode categoricals without data leakage → Scale numerics appropriately → Engineer features that actually matter 𝟒. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞, 𝐒𝐩𝐥𝐢𝐭 & 𝐏𝐚𝐜𝐤𝐚𝐠𝐞 → Recheck data integrity post-cleaning → Split datasets properly (no leakage!) → Version your outputs → Generate validation reports 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: ↳ Clean data = trustworthy insights ↳ Proper prep saves weeks of debugging ↳ Stakeholders trust consistent, validated data ↳ Your models actually work in production Remember: Great models start with great data. Not the other way around. Master data cleaning. Build analyses that actually deliver value. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this framework for your next data project 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

What if I told you that C++26 could eliminate every malloc() and free() call from your embedded IoT stack while making it MORE secure? Summary: Just published my latest deep-dive into how C++26's static reflection can revolutionize embedded protocol development. Using CoAP (Constrained Application Protocol) as a real-world case study, I demonstrate: ✅ Zero dynamic allocation - Complete protocol implementation with compile-time memory determination ✅ Compile-time security policies - Field-level encryption annotations that prevent data leaks by design ✅ DTLS integration - Secure IoT communication without runtime overhead ✅ Safety-critical compliance - Ready for DO-178C, ISO 26262, and IEC 62443 certification The article shows how a temperature sensor CoAP server can be built with deterministic behavior, automatic serialization, and provable security - all generated at compile time. This isn't just academic theory - it's production-ready techniques for the next generation of connected embedded devices where safety and security can't be an afterthought. Call-to-Action: Full technical implementation details, code examples, and security patterns in the article linked below. What embedded challenges are you tackling that could benefit from compile-time guarantees? Hashtags: #CPP26 #EmbeddedSystems #StaticReflection #CoAP #EmbeddedCPlusPlus #SafetyCritical #IoT #DTLS #CompileTime #ZeroOverhead #EmbeddedSecurity #ModernCPlusPlus #EmbeddedDevelopment

Agentic Identity and Access Management (IAM) 🤖 CoSAI just published their paper on Agentic Identity and Access Management. The paper starts from a premise I have been discussing for months. Existing IAM was built for humans and static workloads. Agents break both models because they combine delegated human authority with dynamic tool discovery, multi-step execution, and cross-domain delegation chains that no current system was designed to govern. ↳ Agents need their own first-class identities, not shared service accounts or user impersonation. Every agent should be discoverable, attributable, and independently revocable in the identity registry. ↳ Authorization must become task-scoped and context-aware rather than role-based. The paper advocates for On-Behalf-Of delegation using OAuth token exchange with Rich Authorization Requests so tokens carry intent, not just permissions. ↳ Attestation is foundational. Agents should be cryptographically bound to their execution environment through TEE-backed attestation using Intel TDX, AMD SEV-SNP, or ARM TrustZone, so relying parties can verify not just identity but runtime integrity. ↳ The paper introduces a capability and risk matrix that maps agent autonomy levels (L0 through L5) against capability tiers (read-only through cross-domain write) to determine which controls apply. Higher autonomy and higher capability demand ephemeral identities, explicit OBO delegation, and ABAC/PBAC policy enforcement at every hop. ↳ Delegation chains must attenuate at every hop with full traceability back to a human grant. This maps directly to what Karl McGuinness has been writing about in his series on agentic identity, where authority amplifies without attenuation and audit trails lose the thread at every delegation point. ↳ Governance requires immutable logging of every agent action, token exchange, delegation decision, and policy evaluation, with the ability to reconstruct full delegation chains and "prove control on demand." ↳ The paper lays out a three-phase adoption path. Phase 1 is visibility, discovering and registering all agents as identities. Phase 2 is contextual access with short-lived tokens and ABAC. Phase 3 is full agentic IAM with cross-domain delegation chains, continuous evaluation, and automated discovery of new agents. What makes this paper valuable for practitioners is that it extends existing infrastructure rather than replacing it. Identity stores become agent registries. OAuth servers gain delegation-aware flows. RBAC gets augmented with ABAC/PBAC policies that evaluate intent, context, and risk signals. The structural foundations remain, but they need agent-specific semantics layered on top. This is the kind of industry-wide guidance the ecosystem needs right now. Great work by the Coalition for Secure AI!

Most IAM conversations focus on features. But in 2026, the real question is simpler: Do you have a complete identity system - or just disconnected controls? Because modern identity platforms aren’t built on tools alone. They’re built on integrated components that work together continuously. Here’s what defines a strong Identity Management Platform today: 1. Identity Lifecycle Management (The Foundation) Access should follow the user - not the other way around. ↳ Automated provisioning and de-provisioning ↳ Immediate access removal when roles change or users leave ↳ Support for employees, partners, and customers in one system When lifecycle breaks, risk begins. 2. Access Models That Adapt (RBAC + ABAC) Static roles aren’t enough anymore. ↳ RBAC for structure ↳ ABAC for context (location, device, behavior) Access decisions should reflect real-world conditions—not just job titles. 3. Authentication That Thinks (Not Just Verifies) Login is no longer a one-time checkpoint. ↳ Single Sign-On (SSO) for usability ↳ Multi-Factor Authentication (MFA) for security ↳ Adaptive authentication for real-time risk evaluation Modern IAM doesn’t just authenticate. It continuously evaluates trust. 4. Governance That Proves Control Compliance isn’t about documentation - it’s about evidence. ↳ Continuous access reviews ↳ Policy enforcement aligned with business context ↳ Audit-ready reporting for frameworks like GDPR, ISO, SOC 2 Good governance answers: “Why does this access exist?” 5. Privileged Access Management (Where Risk Concentrates) This is where most breaches begin. ↳ Just-in-time access instead of standing privileges ↳ Session monitoring and approvals ↳ Tight integration with the broader IAM ecosystem If IAM is the control layer, PAM is the risk control center. The shift happening now: IAM is moving from: → Identity storage → Access approvals To: → Continuous identity intelligence → Real-time risk enforcement Final thought A modern identity platform isn’t defined by what it manages. It’s defined by how quickly it can adapt to change, detect risk, and enforce control - without slowing the business down. That’s what separates functional IAM from resilient IAM.