Key Insights on AI Security Debt for Companies

Big Three (AWS, Azure, Google Cloud) consolidating control over security, data, and AI, I’d build a multi-layered security plan that assumes: 1. Cloud providers are not trustworthy. 2. AI-driven security enforcement will be used to restrict access to data. 3. Companies must take back control of their infrastructure or risk losing everything. Ultimate Security Plan to Prevent a Cloud Takeover 1. Infrastructure Control – Get Off Their Grid Hybrid or On-Prem Strategy: Companies must move critical IP and customer data off the Big Three’s cloud. Decentralized Compute & Storage: Leverage self-hosted AI models instead of API-based LLMs. Use alternative cloud providers (e.g., Linode, DigitalOcean, Vultr) for redundancy. Implement private storage solutions (e.g., MinIO, Ceph) to avoid S3 dependency. Data Fragmentation: Encrypt and distribute sensitive data across multiple storage locations, so no single provider has the full picture. 2. Security at the Data Layer – Assume They’ll Try to Take It End-to-End Encryption (E2EE): Encrypt data before it touches cloud storage—providers should only see ciphertext. Use self-hosted key management systems (KMS) instead of AWS/Azure KMS. Zero Trust Data Architecture: No cloud provider gets full access—data is split, sharded, and stored separately. Confidential computing (e.g., Intel SGX, AMD SEV) they can’t decrypt anything. Automated Data Poisoning Defense: Implement honeypots and monitor to detect if AI is being trained on data w/out permission. 3. AI Security – Prevent LLM Takeover Self-Hosted LLMs: Train- run proprietary AI models in-house avoid dependency on OpenAI, Google, AWS models. Poison Their Models: Deploy decoy data that triggers hallucinations in unauthorized AI training attempts. Identify patterns in data scraping attempts and dynamically alter responses. Red Team Their AI If AI security policies are being enforced against you, develop adversarial attacks to force model errors and expose flaws in their enforcement. 4. Operational Security (OpSec) – No Easy Entry Points Network Segmentation & Isolation: Treat cloud infrastructure as a hostile environment and limit cloud-to-internal connections. Air-Gapped Backups: Maintain offline, physically secured copies of critical data in case of cloud lockout. Multi-Cloud Obfuscation: Deploy services across multiple cloud providers with rotating endpoints to prevent surveillance and shutdowns. 5. Legal & Strategic Countermeasures Regulatory Pressure: Push for laws that force cloud providers to separate AI enforcement from cloud security. Public Exposure: Document and expose cloud misconfigurations and breaches to prove they are unfit to control security. Economic Leverage: Encourage mass exodus from centralized cloud providers—they’ll only stop if their revenue is threatened. Don’t Play Defense—Go on the Offensive The strategy isn’t just about protecting data—it’s about breaking Big Tech’s monopoly before they enforce total control.

𝐀𝐫𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 , 𝐀𝐰𝐚𝐫𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐬 𝐭𝐡𝐞𝐢𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐚𝐫𝐫𝐲? AI increases the pace of business. With that it also increases the attack surface. If AI affects your data, decisions or workflows, The risks associated with i are now business risks. Leaders do not have to build models. They need to understand where models fail. I am sharing the 𝟏𝟎 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬, Every leader should understand. 𝟏-𝐃𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲  AI sees customer data, internal docs and logs. Know what data is used and who can access it. 𝟐-𝐌𝐨𝐝𝐞𝐥 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠  Bad data can quietly change model behaviour. Ask how training data is protected. 𝟑-𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧  Inputs can trick models into breaking rules. Controls must exist outside the model. 𝟒-𝐎𝐮𝐭𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 𝐥𝐞𝐚𝐤𝐚𝐠𝐞  Models can repeat sensitive information. Set strict rules on what enters AI tools. 𝟓-𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐚𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬  AI agents run with powerful keys. Least privilege is critical. 𝟔-𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐚𝐧𝐝 𝐓𝐡𝐢𝐫𝐝 ‑ 𝐏𝐚𝐫𝐭𝐲 𝐌𝐨𝐝𝐞𝐥𝐬  Third party models can hide vulnerabilities. Security reviews still apply. 𝟕-𝐑𝐨𝐛𝐮𝐬𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐟𝐨𝐫 𝐀𝐈  Dashboards miss behaviour changes. Expect visibility into inputs and outputs. 𝟖-𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐀𝐭𝐭𝐚𝐜𝐤𝐬 𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬  Small changes can cause wrong results. High risk use cases need extra testing. 𝟗-𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬  Policies define ownership and escalation. Frameworks reduce chaos. 𝟏𝟎-𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬  Know how to pause, roll back and communicate. Treat AI incidents like cyber incidents. AI is not just a productivity tool. Now it is part of your security perimeter. Which of these areas would you prioritize for deeper understanding? --------- Hi, I'm Harris D. Schwartz, Fractional CISO and Cybersecurity Leader. I help CEOs and executive teams strengthen their security posture and build resilient, compliant organizations. With 𝟑𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐍𝐈𝐒𝐓, 𝐈𝐒𝐎, 𝐏𝐂𝐈, 𝐚𝐧𝐝 𝐆𝐃𝐏𝐑, I know how the right security decisions reduce risk and protect growth. If you are planning how your security program needs to evolve in 2026, this is the right time to have that conversation. #CyberSecurity #AISecurity #AIrisk #CISO #SecurityLeadership #CyberRisk

AI isn’t your biggest advantage in 2026. It might be your biggest vulnerability. Everyone is racing to adopt AI. Very few are securing it. Here are 10 AI security failures that could quietly destroy enterprises in 2026: 1. Prompt Injection (Critical) One malicious prompt… and your system does exactly what it shouldn’t. 2. Data Poisoning (Critical) Bad data in → dangerous decisions out. 3. Model Theft (High) Your AI isn’t hacked… it’s copied. 4. Agent Hijacking (High) Autonomous agents executing tasks you never approved. 5. Sensitive Data Leakage (High) Employees feeding confidential data into AI tools without realizing the risk. 6. Model Inversion (High) Attackers pulling hidden training data straight from your model. 7. Supply Chain Attacks (High) One compromised tool = full system exposure. 8. Shadow AI (High) Teams using unapproved AI tools outside your visibility. 9. Adversarial Inputs (Medium–High) Small changes → completely wrong outputs. 10. Hallucination Exploitation (Medium–High) Confidently wrong answers driving real-world decisions. AI doesn’t fail loudly. It fails silently… until it’s too late. The real risk isn’t adopting AI. It’s adopting it without control. The companies that win in this era won’t be the fastest. They’ll be the ones that are secure, governed, and trusted. If your AI made a critical wrong decision today… would you even catch it in time? 👉 Follow Marcel Velica for more insights on AI, cybersecurity, and growth 🔁 Share this with others who need to see it If you want short daily thoughts, quick threat observations, and real-time discussions, follow me on X as well →https://x.com/MarcelVelica

“AI has multiplied the interest rate on the debt you’ve been carrying- your process debt, your data debt, and your technical debt.” It's a statement I've made and perspective I've shared with leaders on many occasions. Here, I've gone ahead and written it out as a full article, complete with symptom spotters and a debt estimator. The article explains how this debt is getting rapidly more costly as AI amplifies not only the risks you are currently exposed to, but also the future opportunities you will stumble trying to seize. What are process, data, and technical debt - and how does AI multiply the interest rate? Process Debt: When steps and handoffs are undocumented or inconsistent, work relies on tribal knowledge. With AI, copilots and agents need predictable flows—ambiguity quickly turns into errors, escalations, and customer friction. Data Debt: Messy, duplicative, or stale records and mismatched definitions create confusion. With AI, bad data becomes confident but misleading answers, leading to poor decisions and eroded trust. Technical Debt: Brittle integrations, legacy parts, and shaky access paths may “mostly work” but remain fragile. With AI, weak links fail loudly—small upstream changes trigger outages, and without strong monitoring, problems surface in public. The article concludes with three likely mistakes leaders will need to avoid (you can't buy your way out of this kind of debt) and four steps leaders can take to begin servicing the debt so they can capture opportunities. Enjoy and share (please) with leaders and change agents who are feeling the burden of the debt and could use language and a path to move forward! TLDR: AI raises the “interest rate” on your process, data, and technical debt, turning once tolerable inefficiencies into visible, compounding, costly risk and unseized opportunity. Undocumented or inconsistent processes make copilots and agents fail at the exceptions humans once handled. Messy, mismatched data gets confidently amplified into wrong answers. Brittle stacks turn minor changes into public outages. Leaders should resist buying AI as a bandage, skipping straight to agents, or neglecting governance and monitoring. Instead, they need to make the debt visible, prioritize a few high-impact fixes (document one process, clean one dataset, stabilize one integration), and build routine debt service into sprints and budgets. Tie each payoff to improved AI outcomes so the technology compounds value rather than risk. With each new AI advancement, the debt gets more costly. #AI #AIDebt #AILeadership

AI by default is now a board-level risk In Jan, Microsoft pulled back. It paused Recall, a Windows 11 AI feature that logged user activity and stored screen snapshots. Framed as helpful, it was received as invasive: 🔸 Security experts raised red flags about surveillance 🔸 Enterprise customers flagged contractual and compliance risk 🔸 Users asked how the feature could be disabled and audited ➡️ Microsoft withdrew Recall and halted Copilot rollouts. A silent shift across enterprises Across communication tools, cloud platforms, productivity suites, AI is embedded by default. Most companies never approved it. Many don’t know they exist. 🔹 Slack start using customer messages and files to train its models. No user notification. Just an email opt-out. 🔹 Zoom updated its terms allowing AI training on user content. Legal and public pressure obliged it to withdraw it. 🔹 SAP deployed its Joule assistant across HR, finance, procurement, and supply chain. 🔹 Salesforce integrated Einstein GPT into sales and service workflows. ➡️ These tools use organizational behavior and generate outputs autonomously, with limited visibility into data use or decision logic. ➡️ Features are released through routine updates, bypassing procurement reviews, risk assessments, disclosures. Vendors assure that customer data isn’t used to train models. But AI oversight requires clarity on how AI operates, data accessed, and whether its behavior can be audited and controlled. The implications for leadership AI by default introduces new exposures: ✔️ Regulatory: AI processing sensitive or behavioral data may activate obligations under GDPR, CCPA, HIPAA. ✔️ Contractual: Features processing client-related content may exceed agreed terms in regulated sectors or professional services. ✔️ Cybersecurity: AI features create new data flows and behaviors absent in existing threat models. ➡️ These concerns show up in litigations, contract negotiations, audits. Clear ownership is key AI governance delivers value when embedded in enterprise risk frameworks, policy oversight, and executive accountability. Leadership should treat this as a strategic function. Boards and executives should ask: ▪️ Which vendors have added AI features? ▪️ Are any AI tools processing organizational data? ▪️ Do contracts and data processing agreement cover these capabilities? ▪️ Have teams or clients been informed? ➡️ Gaps in these areas are reflect active risk. Aligning AI with control and confidence AI’s value runs on trust. Trust requires visibility. Best practice includes: 1️⃣ Demand transparency and auditability from vendors 2️⃣ Review terms in contracts and renewals 3️⃣ Map embedded AI across systems 4️⃣ Assign executive ownership for oversight 5️⃣ Build governance that evolves with AI AI is not the risk. The absence of governance is. Default AI will keep accelerating. Leadership must own visibility, control and trust. #AI #AIGovernance #Boardroom #GenerativeAI #RiskManagement

The End of "Vibe Adoption" 🙅♂️ After two years of FOMO-driven AI deployments, 2026 is the year enterprises either get serious about AI agent security or keep paying the price. The data says many are already paying it. EY found 64% of companies with $1B+ revenue have lost over $1M to AI failures. The paper from AIUC-1 frames the 2026 security landscape around three challenges: 1. The Agent Challenge AI has moved from "assistant" to "autonomous actor." Agents don't just suggest, they execute. Key findings: ↳ Securing AI agents is the #1 concern for 37% of CISOs surveyed ↳ 80% of organizations report risky agent behaviors including improper data exposure and unauthorized system access ↳ Only 21% of executives have complete visibility across agent behaviors, permissions, tool usage, or data access The real production failure mode isn't hallucination, it's over-privileged agents holding long-lived credentials, chaining across systems, and executing actions without clear ownership or containment boundaries. 2. The Visibility Challenge Shadow AI isn't just employees experimenting with ChatGPT, it's a material breach vector operating at scale. ↳ 63% of employees pasted sensitive company data into personal chatbots in 2025 ↳ 1 in 5 organizations reported costly breaches due to shadow AI ↳ The average enterprise has 1,200 unofficial AI applications, 86% are completely blind to their AI data flows ↳ Shadow AI breaches cost $670K more than standard incidents on average 3. The Trust Challenge Prompt injection graduated from research curiosity to production incidents at Microsoft, Amazon, and ServiceNow. What stood out to me: The paper emphasizes a point I've been making in my own writing, that there's a gap between high-level AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act) and what CISOs actually need when it comes to securing AI where it takes action in the enterprise - agents. For AI agents, we need frameworks that move past "do you have an AI risk committee?" to "do your agents validate tool call parameters before execution?" and "can you demonstrate that prompt injection attempts are logged and alerted?" The win conditions boil down to: ↳ Technically grounded, agent-specific frameworks over high-level governance alone ↳ Continuous red teaming over point-in-time assessments ↳ Assume breach and design for containment with least-privilege tool access ↳ Demand supply chain transparency from AI vendors The paper says the era of deploying AI agents based on vibes and good intentions is over. The question isn't whether to adopt AI, it's whether you have the frameworks, processes, and visibility to adopt it safely. Link to the full paper -> https://lnkd.in/eTtjST5S cc: Sanmi Koyejo Brad Arkin Brett Cumming Brian Levine Chris Sandulow Jen Easterly Omar Khawaja et. al

The AI security wake-up call we all needed is here. New research from IBM reveals a concerning disconnect: 82% of executives say secure AI is essential to business success, yet only 24% of generative AI projects actually have security frameworks in place. Even more troubling? Nearly 70% prioritize innovation over security. Critical risks organizations are facing: 🔹 Cybercriminals are leveraging AI just as aggressively as legitimate businesses 🔹Deepfakes and AI-generated phishing attacks are already emerging 🔹"Shadow AI" (employees using unsanctioned tools) is creating significant vulnerabilities 🔹Critical infrastructure integration means exponentially higher stakes The opportunity: Many organizations are still in pilot phases, providing a window to implement proper security measures from the outset. Key recommendations from the IBM/AWS research: 🔹Security must be foundational, not an afterthought 🔹Implement a "secure-by-design" approach across the entire AI pipeline 🔹Update governance models to address AI-specific risks 🔹Leverage strategic vendor partnerships (90% of organizations rely on third-party solutions) The time to act is now—before threat actors become more sophisticated and before vulnerabilities are exploited at scale. Organizations cannot afford to repeat the security missteps of previous technology adoption cycles. A proactive approach to AI security is not just recommended; it's essential for long-term business success. #GenerativeAI #CyberSecurity #AIGovernance #RiskManagement #TechLeadership #IBMSecurity #AWS

NEWS: Okta just exposed AI's biggest blind spot. Most companies aren't ready. I sat down with Shiven Ramji, President of Auth0 at Okta, to find out what's being missed. The problem is bigger than you think: → 80% of breaches already involve compromised identity → 91% of organisations are deploying AI agents → Only 10% have a governance strategy for managing them Traditional security was built for deterministic apps. AI agents are anything but. They access sensitive data at machine speed, far faster than any human ever could. 4 things developers need to get right from day one: 1. Authentication  ↳ Verify the agent is working on behalf of who it claims 2. Human in the loop ↳ Sensitive transactions need async authorisation 3. Fine-grained permissions ↳ Ephemeral access that expires when the task is done 4. Secure connections ↳ No more static API keys scattered everywhere The future Shiv predicts: Memory will become tied to identity. Right now, your context is locked inside each platform. Eventually, you'll want to take your AI memory with you across tools. My takeaway: AI security is identity security. You can't have one without the other. The AI agent market is projected to hit $250 billion by 2034. The companies racing to deploy without solving identity will learn this the hard way. Follow me Alex Banks for daily AI highlights and insights.

Exclusive: Most companies aren't ready for AI-powered threats - Axios Technology and security leaders at major companies have unrealistic expectations for what their AI security plans should look like, a new survey from Accenture finds. Why it matters: If companies aren't aware of the AI security threats they're up against, they're not going to be able to defend against them. By the numbers: 36% of the 2,286 security and technology executives who Accenture surveyed said that AI is outpacing their security capabilities. Only one in five execs said they're confident in their ability to secure their generative AI models against cyber risks. Yes, but: Accenture estimates that 90% of those companies lack the security standards they need to defend against present-day, AI-driven threats. Accenture surveyed executives who work at companies with more than $1 billion in annual revenue. The firm also sanalyzed each of the companies' security practices to determine their AI threat readiness. The big picture: Fending off the emerging wave of AI security challenges will require companies to be proactive and adaptable. Security executives have already found they need to revise their AI-focused playbooks as often as every six weeks. Between the lines: While leaders play catch-up on their AI security plans, companies are plowing ahead in adopting AI anywhere they can. 86% of executives said they are planning to increase their AI investments this year. Yes, but: Protecting against most AI security problems doesn't require cyber pros to completely reinvent the wheel. Accenture recommends companies develop a security governance framework, which will help with concerns around data leaks and privacy. Companies should also invest in AI-driven cybersecurity tools to help automate specific processes and proactively defend against attacks. #cybersecurity #cyberattacks #AI #cyberthreats #threats