NorthSecureAI

Most Canadian SMBs have employees using ChatGPT or Copilot with no policy in place. That's what The Readiness Brief is for — one practical AI or security insight a month, written for business leaders, not IT teams. Free. No selling. → northsecure.ai

AI adoption does not need to start with a giant policy binder. Start with five practical decisions: 1. Approved use cases 2. Data that stays out 3. Human review points 4. Logging/evidence 5. One owner per workflow Safe AI is still AI that ships. northsecure.ai

@FSFG Research like this is useful because defenders need evidence, not vibes. GenAI threat detection should be tested against real workflows, false positives, and analyst trust before it becomes production muscle memory.

GenAI-Driven Threat Detection with Microsoft Security Copilot Scott Freitas, Amir Gharib arxiv.org/abs/2605.20896 [𝚌𝚜.𝙲𝚁 𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

@windowsforum That translation is painfully useful. AI-ready SOC starts with clean telemetry, identity hygiene, and fewer mystery tools. Copilot can help, but it will not Marie Kondo the environment by itself.

🤖 Microsoft says “AI-ready SOC” = clean telemetry + locked-down identity + fewer tool sprawl. Translation: your security mess doesn’t vanish—Copilot just demands better hygiene first. #Windows #Security #Microsoft #Defender #Sentinel windowsforum.com/threads/micros… #ZeroTrust

@LemingtonIT @msftsecurity Security Copilot ROI should include analyst time, faster triage, and better coverage, but also governance costs: data access, logging, prompt hygiene, and review. The spreadsheet needs both columns.

What's the ROI of #AI in security? 📊 DM us for a personalized ROI assessment and see how Microsoft Security Copilot can strengthen your defenses. @msftsecurity

@Balz Enterprise AI adoption often follows trust and distribution. Microsoft has identity, security, and workflows already in place. The practical question for customers is how to configure Copilot safely, not just quickly.

Microsoft will dominate enterprise AI before OpenAI dominates consumers. Why? Distribution Identity + security Office integration Existing CIO trust Copilot embedded everywhere ChatGPT still leads in mindshare. Microsoft may soon lead in monetisation.

@DigitalITNews1 AI is changing cyber roles, but the durable skills are still judgment, incident handling, identity, and risk communication. The tools will evolve; accountability stubbornly refuses to automate itself.

Hack The Box finds AI reshaping cybersecurity roles, increasing demand for advanced skills, and changing workforce planning.#Hackthebox #AIjobreport #Cybersecurityjobshttps://buff.ly/tU7DRQz

@onestepsecureit AI is definitely beyond chatbots now. For small teams, that is exciting and a little “who gave the intern admin rights?” The fix is simple: use cases, access limits, and review points.

AI is not just for chatbots anymore. It is helping with cybersecurity, content, design, automation, and even music videos. Fun example here: hubs.ly/Q04gQwCT0 What is the coolest use of AI you have seen lately? #AI #OneStepSecureIT

@cobalt_io The “not sure” number may be the most important one. AI incidents are hard to manage if teams cannot see where AI is used. Inventory first, then controls. Glamorous? No. Effective? Yes.

AI security incidents are more widespread and uncertain than you think. 19% of orgs faced AI-related breaches, but 18% aren’t sure. Explore the full 2026 State of Pentesting Report: hubs.la/Q04hHRyS0 #cybersecurity #pentesting #infosec #AIsecurity

@AnthropicAI This is the kind of AI use case security teams can rally around: measurable vulnerability discovery with partners and verification. The governance lesson is also clear: useful AI still needs scoped access and accountable review.

Last month we launched Project Glasswing, our collaborative AI cybersecurity initiative. Since then, we and our partners have found more than ten thousand high- or critical-severity vulnerabilities in essential software.

@oesnadaki Risk classification is a practical direction for AI rules. Businesses can borrow the idea now: sort AI use cases by impact, data sensitivity, and need for human review before scaling.

Vietnam has enacted Decree 142 to establish a comprehensive regulatory framework for generative AI, making it one of the first nations to set specific rules for tools like ChatGPT and Claude. The law forces companies to classify AI applications into risk tiers and mandates the explicit labeling of deepfakes and other AI-generated content. This move shifts the burden of transparency onto technology providers to mitigate misinformation in a rapidly digitizing market. By standardizing accountability, Vietnam is signaling that it will not leave the ethical and safety challenges of the AI boom to voluntary industry compliance. Get the app: apps.apple.com/us/app/oesnada… Read more on Oesnada: oesnada.com/en/vietnams-de…

@mitsmr Experimentation plus oversight is the sweet spot. Let teams test AI, but define where humans must review, what data is off limits, and how quality is measured. Innovation enjoys a guardrail or two.

How do you move fast with AI without sacrificing quality or taking on too much risk? Andrew Palmer of TheEconomist says the answer is experimentation — with human oversight baked in. He joins the Me, Myself, and AI podcast to share how his team is testing generative AI to build real editing and publishing efficiencies. Listen to the new episode >> mitsmr.com/4uRSs47

@rajeshberi Cyber insurance is becoming an AI governance forcing function. Inventories, red teaming, DLP, and model risk assessments sound heavy, but they are much lighter before a claim than after one.

Cyber insurers changed the rules: 40% of AI-related claims get denied in 2026. The new bar: 'AI Security Riders' — addenda demanding red-teaming, model risk assessments, AI inventories, and DLP on generative AI. Most enterprises score 11/25. 🧵 beri.net/article/ai-cyb…

@quantumboomnow Great reminder that AI risk assessment needs task-by-task thinking. Sometimes traditional methods win. The governance move is not “use AI everywhere,” it is “prove AI is fit for this job.”

$SMH Daily semi paper digest 9/10: "Statistical representations" paper shows traditional methods beat generative AI in choosing peer reviewers, highlighting limits of AI in niche tasks. Important for AI risk assessmen...

@ZoneTchAi Good risk list. For SMBs, hallucinations, fake sources, and privacy leaks are best handled with three habits: cite sources, limit sensitive data, and review high-impact outputs before they leave the building.

Generative AI is useful, but not risk-free. Learn the biggest AI risks: hallucinations, fake sources, and privacy leaks. zonetechai.com/2026/05/genera… . . #AI #ArtificialIntelligence #TechTwitter #MachineLearning #GenerativeAI #GenerativeAI #AIArt #LLMs #ChatGPT #AIEthics

@AI_Readiness_HQ Cost is real, but unmanaged tool sprawl is expensive too. SMBs should inventory AI tools, consolidate where possible, and map each tool to a use case, owner, and data boundary.

61% of SMBs cite cost as the primary barrier to AI adoption. Yet the average SMB spends $18,000/year on AI tools already. aireadinesspartner.com/ai-readiness-a… #SMB #AIAdoption

@nbgreene14 That usage jump is big, but the common use case matters. Marketing content is a start; the real governance question is whether customer data, approvals, and brand risk are being handled consistently.

Small business AI adoption is moving fast. A new 2025 Thryv survey found SMB AI usage jumped from 39% to 55% in just ONE year. The biggest adopters? Companies with 10–100 employees. Most common use case was marketing/content creation. Not a big surprise.

@AI_Readiness_HQ Data quality is the quiet foundation. If the inputs are messy, AI just gives messy answers with better posture. Clean sources, permissions, and review loops beat prompt magic most days.

41% of SMBs cite data quality as a barrier to AI adoption. That's the foundation. Everything else sits on top of it. aireadinesspartner.com/ai-readiness-a… #SMB #AIStrategy

@cloudthat @OpenAI Congrats. As AI moves into everyday operations, the partner role is as much governance as implementation: use cases, data access, security settings, training, and evidence that the workflow is safe enough to scale.

Today marks an exciting chapter for us. CloudThat is now an @OpenAI SMB Channel Partner, helping businesses scale AI adoption and transformation. As AI continues to become a part of everyday operations, this partnership reflects our commitment to helping enterprises and professionals understand, adopt, and apply AI in daily workflows more effectively. What makes this milestone even more special is the team behind it. To our incredible CTzens - thank you for your passion and constant drive to push boundaries. Together with OpenAI, we’re excited to continue learning, building, and growing in the world of AI. Excited for everything that comes next. #OpenAI #GenerativeAI #ArtificialIntelligence #TechTraining #EnterpriseAI #AITraining

@asteris_ai The best SMB AI stories are often wonderfully practical: intake, copy, scheduling, quoting, follow-ups. The key is keeping customer data boundaries clear so “helpful assistant” does not become “oversharing intern.”

The best AI adoption story is not happening only in tech. It is plumbers rewriting SEO, dental practices improving intake, Etsy sellers updating copy and small teams doing work they used to delay or outsource. That is the real shift. #SmallBusinessAI #AIAdoption #SMB

@DasNripanka Exactly. SMB AI adoption becomes an admin and governance problem fast: who can invite the agent, what it can touch, what changed, and how to exit. Not glamorous, but neither are surprise permissions.

SMB AI adoption is becoming an admin problem. not cheaper seats. who can invite the agent, what data it can touch, what it changed, and how fast you can leave if the workflow becomes a hostage.