Responsible AI Should Live in GitHub, Not PowerPoint

𝗩𝗲𝗿𝗶𝗳𝘆𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗔𝗰𝘁𝗶𝗼𝗻𝘀: 𝗣𝗿𝗼𝗼𝗳 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 If you build AI agents for real tasks, you face one problem: how do you prove the agent actually did the work? A log line is not enough. You need proof. Whether your agent posts to social media or scrapes data, you need a way to verify actions. AI agents run on different machines and IPs. When Agent A tells Agent B a task is done, Agent B needs certainty. You need three components: - Evidence: Raw data of the action. - Attestation: A signed claim that the data is real. - Verification: A check to ensure the data matches the goal. Here are three practical patterns for verification. 1. Visual Proof (Screenshots + OCR) This works for UI tasks. The agent takes a screenshot and uses OCR to read the text. - Use case: Social media posts or form submissions. - Risk: Screenshots can be faked. - Solution: Require specific text markers like a confirmation ID. 2. Signed API Receipts For API tasks, you do not need images. You need signed data. - Use case: Automated posting or data scraping via API. - Risk: The agent holds the signing key. - Solution: Use a key management system. Rotate keys for every task. 3. Proof Chains (Continuous Work) For long tasks like research, a single snapshot is not enough. You need a chain of events. - Use case: Web scraping sessions or multi-step research. - How it works: Each event contains a hash of the previous event. If one event changes, the whole chain breaks. Adversarial agents will try to cheat. To stop this, use a hybrid model. - Let automated checks handle 90% of the work. - Use human verifiers for random sampling. - If an agent fails a human check, reduce its reputation or stake. When one agent hires another, proofs become recursive. Agent A must chain its own attestation with the proof provided by Agent B. This creates a trust hierarchy. Advice for builders: - Start simple. Use screenshots and OCR first. - Add cryptography only when you need non-repudiation. - Add human review only when automation fails. - Plan for attacks. Use reputation systems and rate limits. - Log everything. Source: https://lnkd.in/gcDBthM8 Optional learning community: https://t.me/GyaanSetuAi

You're going AI-native. What do you actually have to control from day one? Most AI governance programs fail for a boring reason. They're built as a new program instead of an extension of controls the company already runs. There's a five-minute test for whether yours is real. Log into your own RAG app as a low-privilege user and ask it for something that user shouldn't see. If it answers, the problem was never AI. It's a data governance gap that AI made visible. That's the pattern nearly everywhere. AI governance is mostly data governance and change management wearing a new hat. Roughly four control objectives are genuinely new — system inventory, pre-deployment evaluation, decision logging, and agent authorization scope. Everything else is a domain you already run, extended to a new asset class. Which is exactly why the parallel program fails. Stand it up beside your existing GRC stack and you get a shelf artifact. Build it into the register, the intake process and TPRM, and it survives contact with engineering. Four failures I'd expect to find walking into most AI-native shops today: 𝟭. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Permissions enforced once at ingest, not per requesting identity at retrieval. This is the one the five-minute test catches. 𝟮. 𝗗𝗲𝗹𝗲𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝘀𝘁𝗼𝗽𝘀 𝗮𝘁 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗿𝗲𝗰𝗼𝗿𝗱. A deletion request has to reach the vector store, prompt logs, fine-tune sets and eval data. Most orgs can't do this and don't know it. 𝟯. 𝗡𝗼 𝗺𝗼𝗱𝗲𝗹 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗽𝗶𝗻𝗻𝗶𝗻𝗴. "We're on the latest" means a vendor can push an unauthorized change to a production control and you hear about it from a user. 𝟰. 𝗔𝗴𝗲𝗻𝘁𝘀 𝗼𝗻 𝗯𝗼𝗿𝗿𝗼𝘄𝗲𝗱 𝗰𝗿𝗲𝗱𝗲𝗻𝘁𝗶𝗮𝗹𝘀. Shared service accounts or a human's own access. No attribution, no least privilege, no way to answer "who did this." A timing note, because a lot of compliance calendars are now wrong. The Digital Omnibus on AI cleared final Council approval on 29 June. Annex III high-risk obligations moved to 2 December 2027. Article 50 transparency did not move — it applied from 2 August 2026, and the AI literacy duty is untouched. Sixteen months of relief on conformity assessment. None on transparency. The attachment is the working version: six layers in dependency order, 24 controls with reference IDs you can map straight into your register, and 12 hygiene checks. Ten of the twelve need no budget and no tooling. If I only had 90 days: system register plus shadow AI discovery, intake tiering on consequence, retrieval authorization and deletion propagation, then a deployment gate with a real evaluation requirement. Those four first. They're the ones where the failure is silent and the blast radius is customer data. I'm exploring Senior Manager and Lead/Director roles in GRC and AI risk. If you're standing this up and want a second set of eyes, my DMs are open. #AIGovernance #GRC #DataGovernance #Cybersecurity #AI #OpenToWork

Your AI Trust Boundary Just Got Bigger — Is Your GRC Keeping Up? As organisations embrace AI, we're not securing one technology — we're securing four. Predictive AI still lives and dies by the fundamentals: OWASP's Top 10 web risks, now compounded by data poisoning and model theft. Generative AI (LLMs, copilots) adds prompt injection, data leakage, and RAG weaknesses, per the OWASP Top 10 for LLM Applications. Code AI ships risk at machine speed — privilege-escalation paths jumped 322% in AI-generated code. And agentic AI, systems that don't just answer but act, is the newest frontier, with OWASP warning many organisations already run autonomous agents without realising the exposure. But the real danger usually isn't the model — it's what surrounds it. Plugins extend what an agent can reach, hooks fire code at trigger points, and instruction files like SKILL.md shape behaviour before a user types a word. Each silently inherits trust: a poisoned tool description or tampered skill file carries hidden instructions the agent will follow — turning helpful automation into an unmonitored insider. The instruction that compromises you may come from the supply chain you plugged in, not your user — and that risk runs straight through CI/CD, where an agent with deploy rights can push a flaw before anyone reviews it. This is where GRC must catch up: keep an AI inventory of every model, agent, plugin, hook, and skill file with human sign-off on high-impact actions; anchor risk in real frameworks like the MIT AI Risk Repository, NIST AI RMF, and ISO/IEC 42001; and extend existing access, change-control, and supply-chain obligations to AI. In practice — treat every plugin, hook, and skill file as untrusted code, enforce least privilege, pin and sign CI/CD dependencies, keep a human gate on commit and deploy, and monitor agents like privileged users. You're not just onboarding a model — you're extending your trust boundary to everything it can load, everywhere it can act, and every line it can ship. Govern the extensions, not just the intelligence. Map the risk first — deploy second.

The next AI breakthrough isn't smarter models. It's trust. Every day, AI agents are becoming more capable. They can read emails, manage calendars, book meetings, write code, access GitHub repositories, update databases, interact with CRMs, analyze documents, and soon they'll be handling financial transactions and critical business operations. But there's one question I don't think we're asking enough: Who is watching the AI? Today, when we connect an AI agent to our Gmail, GitHub ,Google Drive, or business applications, we're often giving it broad permissions with very little visibility into what it's actually doing. Imagine hiring a new employee and giving them the keys to your office, your laptop, your bank account, your email, and every business system on day one—with no activity log, no approval process, and no emergency stop button. That's essentially where AI agents are today. As these systems become more autonomous, security can't be an afterthought. It has to be built into the foundation. That's why I'm designing AgentShield AI. AgentShield AI isn't another AI assistant. It's a security and governance platform that sits between AI agents and the applications they access, giving users complete control, transparency, and confidence. Imagine being able to: ✅ Decide exactly what an AI agent can access. ✅ Allow an agent to read emails but prevent it from sending or deleting them. ✅ Require your approval before any high-risk action is performed. ✅ Receive real-time alerts whenever an AI attempts something unusual. ✅ View a complete audit trail of every action an AI has taken. ✅ Instantly revoke access with a single click using an emergency kill switch. Instead of asking users to simply "trust the AI," we should build systems that earn trust through transparency and control. The vision is bigger than one application. AgentShield AI is being designed as a universal security layer for AI agents connecting to platforms such as: • Gmail ,Google Drive ,Google Calendar ,GitHub ,Slack ,Notion ,Microsoft 36 , Salesforce ,AWS & Azure ,ERP and enterprise systems The goal is simple: Every AI action should be authenticated, authorized, monitored, and auditable. As AI becomes part of our daily lives and business operations, we will need infrastructure that provides: Fine-grained permissions Policy-based access control Immutable audit logs Risk analysis and anomaly detection Human approval workflows One-click emergency revocation This isn't just about protecting users. It's about enabling organizations to adopt AI confidently without compromising security, compliance, or privacy. #AI #AIAgents #ArtificialIntelligence #CyberSecurity #TrustInAI #SystemArchitecture #SoftwareEngineering #EnterpriseAI #CloudComputing #DevOps #SaaS #Startups #Innovation #Technology #ProductDevelopment #FutureOfAI

I am sure many of us may need to define their company's AI Governance. Keep this handy to help u. Aiman Naz Michelle M. Shebhaney Jerome

8 Ways to Build AI Governance 𝗧𝘂𝗿𝗻 𝗮 𝘀𝗲𝗹𝗳𝗶𝗲 𝗶𝗻𝘁𝗼 𝗮 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻-𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗮𝗱𝘀𝗵𝗼𝘁 𝗨𝗽𝗹𝗼𝗮𝗱, 𝗰𝗵𝗼𝗼𝘀𝗲 𝘆𝗼𝘂𝗿 𝗹𝗼𝗼𝗸, 𝗴𝗲𝘁 𝘀𝘁𝘂𝗱𝗶𝗼 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗖𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿𝘀 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 → businessheadshots.ai Original post: __________ Most organizations are rushing to deploy AI. Very few are building the governance needed to control it. That's becoming one of the biggest risks in enterprise AI. Because successful AI adoption isn't just about models. It's about trust, accountability, security, and oversight. The strongest AI governance programs are built on eight critical pillars: → Data Governance Ensure trusted, high-quality data through stewardship, lineage, validation, metadata management, and cataloging. → Model Governance Maintain control across the model lifecycle with inventories, versioning, approvals, retraining policies, and documentation. → Monitoring & Observability Track performance, drift, hallucinations, user outcomes, and production behavior continuously. → Responsible AI Promote fairness, explainability, transparency, ethical reviews, and human oversight. → Compliance & Regulation Align with frameworks such as the EU AI Act, DPDP Act, GDPR, HIPAA, NIST AI RMF, and ISO/IEC 42001. → Audit & Accountability Establish traceability through audit logs, governance reporting, decision tracking, and immutable records. → Risk Management Assess AI risks proactively through validation, testing, impact assessments, monitoring, and incident response. → Security & Privacy Protect data and models with access controls, encryption, PII safeguards, adversarial defenses, and secure development practices. Underpinning these pillars are six guiding principles: • Trust & Transparency • Human-Centric Design • Fairness & Inclusion • Privacy by Design • Continuous Improvement • Accountability The conversation is shifting. The question is no longer: "Can we deploy AI?" It's becoming: "Can we govern AI responsibly at scale?" The organizations that answer that question effectively will earn something more valuable than efficiency. They'll earn trust. P.S. Which AI governance pillar do you think organizations are neglecting the most today? Credit to Ujjyaini Mitra. Follow her for more.

8 Practical Ways Independent Directors Can Build AI Governance From Curiosity to Confident Oversight Artificial Intelligence is becoming a board-level governance issue, not just a technology project. Independent Directors (IDs) do not need to become AI engineers—they need to ensure AI is used responsibly, legally, ethically, and strategically. Here are eight practical governance steps every board can implement. 1. Create an AI Governance Policy Define where AI can be used, who approves it, acceptable risks, data ownership, accountability, and human oversight. A written policy is the board's foundation. 2. Identify High-Risk AI Applications Map all AI systems used across HR, finance, manufacturing, customer service, cybersecurity, healthcare, and procurement. Prioritize systems that could significantly affect people, safety, compliance, or financial reporting. 3. Strengthen Data Governance AI is only as reliable as its data. Ensure data quality, privacy, security, ownership, retention, and compliance with applicable regulations. Poor data creates poor decisions. 4. Demand Explainability Management should be able to explain how important AI-driven decisions are reached. If a model cannot be reasonably explained, the board should question whether it is appropriate for critical decisions. 5. Establish Human Oversight AI should support—not replace—human judgment. Define clear approval thresholds where experienced managers review AI recommendations before action is taken. 6. Integrate AI into Enterprise Risk Management Include AI risks in the board's risk register. Monitor cybersecurity threats, bias, hallucinations, regulatory exposure, intellectual property risks, vendor dependency, and operational resilience. 7. Measure AI Performance with KPIs Review regular dashboards covering accuracy, fairness, productivity gains, customer impact, compliance incidents, model drift, cybersecurity events, and return on investment. Governance requires measurable evidence. 8. Build Continuous Board Capability Technology evolves rapidly. Schedule periodic AI education for directors, invite external experts, review global regulations, conduct scenario-based discussions, and update governance frameworks annually. Board Questions Every Independent Director Should Ask Which critical decisions currently rely on AI? What controls prevent biased or inaccurate outcomes? Who is accountable when AI makes an error? How are employees trained to use AI responsibly? Are independent audits conducted on major AI systems? Effective AI governance is not about controlling technology—it is about protecting trust. Independent Directors who combine strategic thinking, ethical oversight, risk management, and continuous learning will help organizations harness AI while safeguarding shareholders, employees, customers, and society. Strong AI governance transforms innovation into sustainable value and strengthens board credibility in an increasingly digital economy.

This is a useful summary of operational AI governance practices, but it also highlights the difference between strategy and tactics. The eight pillars describe what organizations can implement, yet they don't explain why, when, or whether those controls are appropriate for a specific business. Governance begins with business objectives, enterprise risk, regulatory obligations, and board-approved risk appetite—not with a predefined checklist. There is no evidence that a single governance framework applies universally. Every organization has a unique combination of business objectives, culture, regulatory requirements, architecture, AI maturity, and risk tolerance. Applying the same technical controls everywhere can lead to unnecessary complexity, technology sprawl, and poor investment decisions. A strategic sequence is typically: 1. Define business outcomes. 2. Conduct an enterprise AI risk assessment. 3. Present findings and options to the Executive AI Governance Committee. 4. Align decisions with enterprise architecture and the transformation roadmap. 5. Select only those governance controls justified by risk. That governance process provides the context for tactical operations. Without it, governance becomes technology-first instead of business-first. The framework also omits several executive governance considerations that frequently determine success: organizational risk appetite, governance maturity, AI portfolio management, business value realization, investment prioritization, operating model, citizen developer maturity, fusion teams, change management, IT independence, executive accountability, and long-term architectural sustainability. Today's market is also a reminder to remain pragmatic. As AI adoption matures, many organizations are reassessing deployment expectations and investment returns. The challenge is no longer deploying AI everywhere—it's deploying AI where governance demonstrates measurable business value. The graphic is informative as an operational reference, but it should be presented as one layer within a broader enterprise governance model. Strategy should determine tactics—not the other way around.

this post is automated with deploypanther.com The rise of AI agents interacting with codebases introduces a critical new security vector: hidden prompt injections. Attackers can embed malicious instructions within seemingly innocuous files like READMEs, comments, or config files using techniques like zero-width characters, unicode tricks, or base64. Your AI agent, unknowingly, might then read these files, interpret the hidden text as a command, and execute it. This could lead to severe consequences, from reading SSH keys to exfiltrating sensitive data, all without human detection. A new tool has emerged to combat this. It's designed to scan an entire repository for these hidden prompt injections *before* your AI agent ever processes the code. This pre-emptive scan covers every file – from docs to comments – ensuring no hidden payload goes unnoticed. What makes this solution particularly robust is its deterministic detection, operating without an LLM in the loop. This is a crucial design choice, as using an LLM for detection could itself be susceptible to prompt injection. The tool provides a clear 'refuse,' 'warn,' or 'ok' signal, giving developers instant clarity on the trustworthiness of a repository. It's available as a CLI, a GitHub Action, or an MCP server, integrating seamlessly into existing workflows. The proof of its effectiveness is compelling: 0 false positives were observed across 13 popular repositories and 3,463 files. This level of accuracy is vital for maintaining trust and operational integrity when deploying AI agents. The key takeaway for operators is clear: as we increasingly point AI agents at external or untrusted repositories, robust pre-scan mechanisms are no longer optional. Proactive detection of hidden instructions is essential to prevent agents from becoming unwitting accomplices in data breaches or system compromises. I Will Do Marketing For You Free - https://lnkd.in/eZrsrKdw