Online Skill Verification

Prompt to Production in Minutes: How Vibe Coding Will Make Your GRC Program Obsolete Soon 🚀🚀🚀 Your GRC team is about to be hit by a tsunami of vibe-coded applications generating vulnerabilities faster than you can say "let me update that risk register." The era where devs meticulously build systems over months is being replaced by AI generating entire apps based on vibes in minutes. And guess who's responsible for securing this mess? That's right - us (and everyone else lol). What this will mean for GRC teams SOON: 🚀 Application development cycles no longer take weeks, they take HOURS. Your quarterly evidence collection model? Dead on arrival. ⚡ Vulnerability landscapes changing at hyperspeed. Your annual pentest? About as useful as a seatbelt on a ebike. 💸 Traditional GRC processes can't keep pace, and we're spending millions on attestations documenting systems that have already changed 47 times since documentation. When new-age devs can say "make me a banking app to disrupt everything" and get functional code in minutes, we can't keep using spreadsheets and screenshots as our primary GRC tools. Your risk register isn't supposed to remain a collection of "AI hallucination risks" that everyone auto-accepts. ✅✅✅ GRC Engineering isn't optional anymore, three ways to catch the right vibes: 1️⃣ Embed security guardrails directly into prompt engineering Your developers are telling AI "make me an app with these vibes" - but who's ensuring those prompts include "that follows our security architecture"? Security requirements must be baked into prompt templates BEFORE code generation, not after. Leverage system prompts to help. 2️⃣ Replace point-in-time evidence with continuous verification If code can be regenerated in minutes, your annual access review is meaningless. Build API-driven evidence pipelines that verify security posture in real-time. When your app changes 50 times a week, your compliance evidence needs to keep pace. 3️⃣ Create a unified taxonomy between AI coding systems and GRC data When AI builds your app, the trail connecting vulnerabilities to controls to risks becomes blurry. Establish a common language so your system understands that "this vibe-coded feature impacts these compliance controls." Without this, your technical debt spirals into the void. We're no longer just monitoring human-created vulnerabilities but also machine-generated ones with entirely new patterns. It's still fairly niche but in the near(er) future, a new generation of Cursor-enabled developers will need a strong GRC team to avoid deploying insanity to production every 10 sec. Start thinking about it now! #GRCEngineering #VibeCoding

Process validation gets treated like a wedding. Everyone shows up. The SMEs reappear. Operators follow the SOP with unusual precision. Every detail gets attention, and the process performs beautifully because the whole company is watching. Then the guests go home. That is where validation actually gets interesting. Operators rotate. Raw material lots change. Night shift makes judgment calls that never came up during PPQ. Six months later, the process running on a random Tuesday may look very different from the one everyone watched during qualification. That is the gap PIC/S is now pushing harder on. PIC/S hasn't touched this guidance since 2007. The new version takes effect this October. The old comfort of “three consecutive batches and you’re done” is giving way to a more risk-based expectation. The number of batches needs justification, and ongoing verification matters because validation is not a single performance. It is evidence that the process stays in control over time. A process that only works when everyone is watching was never very well validated. The real test is whether it still behaves the same way in month eight, under normal conditions, when nobody is treating the batch like a special occasion. #ProcessValidation #GMP #PICS #QualitySystems

A Senior Data Engineer candidate was asked to design an incremental ingestion pipeline during his interview at Google. Another candidate in a different loop at Facebook got the same prompt. CDC pipelines look simple until you add one layer of reality: – Add late arriving updates? Now you need watermarks, reprocessing windows, and correctness guarantees. – Add duplicates and retries? Now idempotency becomes the whole game. – Add schema changes? Now your pipeline breaks at 2 AM unless you plan compatibility. – Add backfills? Now you are doing surgery on live tables without double counting. – Add merge cost? Now your “incremental” job is slower than a full reload. Here’s my checklist of 15 things you must get right when building incremental ingestion with CDC: 1. Start with the business contract → Define what “correct” means: latest state per entity, full history, or both. This single decision changes your table design, merges, and backfills. 2. Choose the right ingestion model: snapshot + CDC vs pure CDC → Snapshot + CDC is safest for bootstrapping and recovery. Pure CDC is leaner but brittle if you miss events. 3. Pick a stable primary key strategy → If your upstream keys are messy, create a durable surrogate key. Your entire dedupe and merge logic depends on this. 4. Capture an ordering signal you can trust → Use a reliable change version: log sequence number, commit timestamp, or monotonically increasing version. Avoid “updated_at” unless you fully trust the source. 5. Design for idempotency from day one  → Assume every event can arrive twice. Your writes must be safe to re-run without changing results. 6. Handle deletes explicitly → CDC isn’t just inserts and updates. Support tombstones or delete flags and define how downstream tables interpret them. 7. Preserve raw events before you transform → Land the raw change feed in a bronze layer. If downstream logic is wrong, raw becomes your rewind button. 8. Build a dedupe rule that survives retries and replays → Dedupe by (primary_key + change_version) or (primary_key + event_id). If event_id is missing, generate one deterministically from the payload plus version. 9. Use watermarks, but never trust them blindly → Watermark = “I have processed up to here.” Still keep a safety lookback window because late data is guaranteed in production. 10. Implement a reprocessing window for late arrivals  → Recompute the last N hours or days on every run based on observed lateness. This is the simplest way to get correctness without constant firefighting. 11. Plan schema evolution with compatibility rules → Decide: backward compatible only, or allow breaking changes with a controlled rollout. Use versioned schemas and block unsafe changes automatically. (Continued in comments.)

The new consulting edge isn't AI. It's knowing when your AI is wrong. Every consultant has been there: You ask AI to analyze documents and generate insights. During review, you spot a questionable stat that doesn't exist in the source! AI hallucinations are a problem. The solution? Implementing "prompt evals". → Prompt evals: directions that force AI to verify its own work before responding. A formula for effective evals: 1. Assign a verification role → "Act as a critical fact-checker whose reputation depends on accuracy" 2. Specify what to verify → "Check all revenue projections against the quarterly reports in the appendix" 3. Define success criteria → "Include specific page references for every statistic" 4. Establish clear terminology → "Rate confidence as High/Medium/Low next to each insight" Here is how your prompt will change: OLD: "Analyze these reports and identify opportunities." NEW: "You are a senior analyst known for accuracy. List growth opportunities from the reports. For each insight, match financials to appendix B, match market claims to bibliography sources, add page ref + High/Med/Low confidence, otherwise write REQUIRES VERIFICATION.” Mastering this takes practice, but the results are worth it. What AI leaders know that most don't: "If there is one thing we can teach people, it's that writing evals is probably the most important thing." Mike Krieger, Anthropic CPO By the time most learn basic prompting, leaders will have turned verification into their competitive advantage. Steps to level-up your eval skills: → Log hallucinations in a "failure library" → Create industry-specific eval templates → Test evals with known error examples → Compare verification with competitors Next time you're presented with AI-generated analysis, the most valuable question isn't about the findings themselves, but: 'What evals did you run to verify this?' This simple inquiry will elevate your teams approach to AI & signal that in your organization, accuracy isn't optional.

There are 1.1M credentials but our latest research finds that only 12% offer significant wage gain earners wouldn’t have otherwise gotten. The Burning Glass Institute is launching the Credential Value Index to show which ones work, evaluating the outcomes from 23,000 non-degree credentials from over 2,000 providers, including every certification in America—from Coursera digital marketing certificates to OSHA certifications. To see whether they actually deliver for workers, we analyzed how each changed the course of the careers of 7 million people who had earned them. While only 1 in 3 credentials meet a minimum threshold vs. counterfactual peers for either boosting wages, facilitating career changes, or moving people up within their field, we still found 8,000 credentials that really move the needle for workers—often in ways that are transformative. The top decile of credentials yields annual wage gains of nearly $5,000 vs. counterfactual peers, increases by 7x vs. bottom credentials the chances of switching jobs into an aligned career, and boosts by 17x the probability of an earner’s getting promoted within their current field. We found wide variances in outcomes even for the same credential across named providers–and across the portfolio of credential offerings of even high-reputation providers. That says that learners can’t just trust brands and they can’t just trust that a credential will help just because it’s in a high-paying field. Instead, they need real data to help them make informed decisions. Our goal in this work is practical: to put these evaluations in the hands of workers and learners, employers, education institutions & training providers, and policymakers. The Credential Value Index–available through our Navigator site available on https://lnkd.in/e_BTX9bs –makes all 23,000 evaluations accessible to the public, with easy-to-understand metrics of performance, comparisons with other credentials, and helpful context, like which roles earners find themselves working in, which employers they’re working for, and which skills they master along the way. Our research is summarized in an American Enterprise Institute working paper which I coauthored with AEI senior fellow Mark Schneider and Burning Glass Institute colleagues Shrinidhi Rao, Scott Spitze, and Debbie Wasden. You can find it on https://lnkd.in/ezynMA-v. I want to express my deep thanks to Ellie Bertani, Matt Zieger, and the GitLab Foundation for all they have done to support this initiative. I am grateful for your partnership. And a big thank you to Patti Constantakis and Sean Murphy at Walmart for the opportunity to test this framework in a real-world laboratory. Finally, the Credential Value Index builds on a close partnership with Jobs for the Future (JFF). Many thanks to Maria Flynn, Stephen Yadzinski, and their terrific team. #education #careers #highereducation #learning #skills

Telling if a video is AI-generated is challenging—experts estimate detection rates at 70-80% for humans alone—but a methodical approach helps. Can you spot the subtle tells? The uncanny movements? The impossible physics? This challenge isn't just a game; it's a crucial skill in the age of deepfakes and misinformation. 💡 How to Systematically Check a Video ⇩ Follow these steps every time you encounter suspicious content (e.g., viral social media clips): 1) Verify the Source First (1-2 minutes) Who posted it? Reverse-image search a frame using Google Lens or TinEye. If the original is from an AI tool demo or lacks context (no credible news outlet), be wary. 2) Assess Basics: Length and Quality (30 seconds) Is it under 10 seconds or blurry? Play at normal speed first—if it feels "off" visually, proceed to details. 3) Scrub and Zoom: Hunt for Visual Tells (1-2 minutes) Pause frequently. Zoom into hands, text, faces, and backgrounds. Slow to 0.25x speed to catch motion glitches. Use your phone's zoom for extra scrutiny. 4) Listen Critically: Audio Check (30 seconds) Mute, then unmute. Note any robotic timbre or sync slips. Real audio has ambient noise; AI often feels sterile. 5) Cross-Check Context and Physics (1 minute) Does the scenario make sense (e.g., casual phone vid vs. polished ad)? Test physics by mentally tracing shadows or trajectories. 6) Stack the Evidence and Use Tools (Ongoing) One red flag? Could be real. 3+? Likely AI. Upload to detectors like Truepic or Deepware for a second opinion. Always share doubts—e.g., "This looks AI, thoughts?" Remember, AI detection tools lag behind generation tech, so skepticism is your best tool. Are your eyes sharp enough to see the difference? Take the test and find out! P.S.: Feel free to share your score. How many out of 5 did you get correct?

A few updates, well-timed with re:Invent happening this week...   Trust is one of the most important components of scaling #AI systems. It is also one of the hardest to build. I spoke with Fast Company’s John Pavlus about this and about how we are partnering with Amazon Web Services (AWS) to bring new sources of trust into the enterprise with automated reasoning.   Many organizations find themselves in a stalemate. Users do not quite trust a system enough to use it for real work. At the same time, they cannot build that trust because the system is not used in meaningful contexts. The result is a long-running pilot that never quite becomes production.   A practical way to break that stalemate is “trust but verify”. Pair AI systems with independent checks that verify whether outputs follow the rules, regulations and intent that matter for the business.   That is the focus of our work with #AWS on automated reasoning in Bedrock and AgentCore. At PwC, we are applying this with clients in regulated and high-consequence domains. Our Regulated Content Orchestrator for life sciences uses AI to draft materials for new drugs, then applies contextual grounding, accuracy checks and automated reasoning checks before legal review. Our EU AI Act evaluator uses a similar pattern to classify AI systems and verify that the classification lines up with the act. We are also using automated reasoning in cloud policy and infrastructure as code, where clients want evidence that certain failure states, such as public access to sensitive data, cannot occur under the specified configuration.   Trust and verification help break the stalemate together. When they do, organizations can move beyond pilots and into sustained use of AI at scale. cc. Byron Cook, Swami Sivasubramanian, Ruba Borno.

Being exceptional at interviews has virtually nothing to do with being exceptional at the actual job - but hiring managers keep making this same expensive mistake. You're rejecting talented problem-solvers because they didn't perform well in scripted Q&A sessions, then wondering why polished new hires can't execute under pressure. Still asking "tell me about a time you demonstrated leadership" like it's 1995, then shocked when the STAR method expert can't deliver results. What actually predicts job performance: Give real problems to solve in real-time. Not prepared case studies - actual messy scenarios where you observe genuine thinking process. Offer paid trial projects for 1-2 days. Real work performance tells you more than six interview rounds. Have finalists shadow the team for half a day. Watch how they naturally interact, what questions they ask, how they process information. Ask them to critique something your company does. Reveals how they think, not how well they memorized frameworks. Candidates crushing your interview process might just be great at interviews. The ones who can excel at the job might bomb your outdated assessment. Sign up to my newsletter for more corporate insights: https://vist.ly/4etcm #hiringprocess #interviews #recruitment #hiring #talentacquisition #hiringmanagers #interviewskills #jobinterview #hiringstrategies #recruitmentprocess

If you think "I use ChatGPT" is an AI skill, scroll past. But if you're a non-tech professional who's worried about staying relevant in the next 3 years, this is for you. The uncomfortable reality? Most people aren't learning AI skills. They're just learning AI tools. And there's a massive difference. Here's what professionals are getting wrong: ✖️ Treating AI like a search engine with better grammar. ✖️ Using it for tasks, not for building leverage. ✖️ Confusing "I use it daily" with "I know how to use it." Here are 5 AI skills that actually matter and most people have no idea about: ✔️ AI Output Auditing: AI doesn't tell you when it's wrong. It just sounds very confident. The ability to fact-check AI outputs and catch "hallucinations" is now a core professional skill. ✔️ Contextual Prompt Translation: Forget basic prompting. The real skill is taking a messy, complex business problem and breaking it into a precise, structured instruction set AI can actually execute. Role constraints, tone, audience, outcome: all of it packaged before you hit enter. ✔️ No-Code Workflow Automation: Tools like Zapier and Make let you build full automated workflows with zero coding. Auto-routing data, syncing apps, triggering actions without lifting a finger. This one skill can give you back 5–10 hours every single week. ✔️ Human-AI Task Orchestration: Knowing WHAT to hand off to AI vs. what to keep human: that's a strategy skill. Research, first drafts, formatting → AI. Judgment calls, relationships, ethics → you. ✔️ AI-Assisted Data Storytelling: AI can process numbers. Your job is to turn those numbers into decisions people can act on. Knowing what story the data is actually telling and how to present it is still 100% human territory. The World Economic Forum says 170 million new roles will emerge by 2030. Every single one will require working with AI. The question isn't if you'll use it. It's whether you'll know how. Which of these are you already working on? Drop it in the comments 👇 #aiskills #careergrowth #futureofwork #jobsearch #dreamjob #ai

The recent Hindustan Times article by Dr. Arvind Gupta and Dr. Priyank Narayan captures a reality many of us in tech and career growth have experienced firsthand: our skills, achievements, and credentials still live in silos. Your degree sits in one system. Your certifications are in another. Your workplace training is somewhere else entirely. And none of it moves seamlessly with you as your career evolves. That’s exactly what the proposed National Learning Wallet aims to fix. Why it matters: * Your learning becomes portable, verifiable, and future-ready , not locked to a single institution. * Hiring gets faster, with less time wasted on verification and paperwork. * Professionals gain global mobility with trusted, digital proof of skills. * Career transitions become smoother as your growth journey travels with you. In a world where skills evolve faster than job titles, a unified and credible learning profile isn’t just an innovation, it’s the foundation for the future of work. If you care about career growth, talent strategy, or future-ready workforces, this is worth a read. 🔗 https://lnkd.in/gEiTKBMA #CareerGrowth #FutureOfWork #Skills #DigitalIndia #TalentMobility #LearningEcosystem