Announcing the Results of the @Thinkers360 2026 AI #Trust Index Survey
An annual pulse check on #AI end user and AI provider sentiment on various aspects of trust in AI >> https://t.co/TMDSd5Fq3E
@Babson@TDav@Innogentic@NicholasDEvans #tech #digital #innovation #disruption #leadership #business #management #governance #CIO #CTO #CDO #CEO #AIStrategy #digitalstrategy #genAI #AgenticAI #digitaltransformation #digitaltrust
**Not every damn thing needs to be an AI agent!**
There. I said it. 😂 Again.
I've been having conversations with people who are building AI agents and worrying about how to secure them.
And sometimes I find myself wondering...
1/6
AI is transforming software development and exposing new security challenges.
Tom Gillis and Will Eatherton of @Cisco join @NickLippis to discuss AI-powered code reviews, vulnerability discovery, and why security practices must evolve.
🎧 Full episode: https://t.co/y5h9yqLdn2
The Biggest Hotel And Hospitality Technology Trends For 2027
#AI#agents are becoming a new route to #hotel#bookings. In 2027, hotel leaders will need to protect their access to #guests, put #automation to work and secure systems that increasingly transact with #software.
The payoff should be better margins and more time for human #hospitality.
https://t.co/zpFkls2tRD
AI Defense Has Moved From Alert Triage to Scale Economics
Venkata Koppaka’s https://t.co/sMBpdWYtUL post on hyperscale defense is a strong and timely piece of thinking. Credit to Venkata for framing the issue in a way that moves the conversation beyond the usual “AI attackers are coming” storyline and into the harder operational truth: the next generation of attacks will not always succeed because they are invisible. They will succeed because they are buried inside massive volumes of otherwise legitimate activity.
The article is worth reading: https://t.co/sgfSwKUPdg
I agree with the perspective being brought forward here. The problem is not simply that attackers are getting smarter. It is that the economics and scale of attack are changing. If thousands of agents can probe infrastructure, identities, endpoints, services, dependencies, and misconfigurations in parallel, then a SOC built around filtering, sampling, and escalating only the most obvious alerts is already operating at a disadvantage.
For years, enterprise security operations have depended on reducing noise. That made sense when humans were the bottleneck and attackers moved at human speed. But in an agentic attack model, filtering can become a liability. The evidence that gets dropped may be the very evidence that explains the attack path.
This is the same architectural lesson we have seen in cloud computing, data platforms, and now AI operations: you cannot manage what you cannot observe, and you cannot govern what you cannot explain. Security teams need telemetry at the speed and scale of the systems they defend. They also need automation that can reason over that telemetry without removing humans from the decisions that matter.
There are solutions in the market attacking pieces of this problem. SIEMs such as Microsoft Sentinel and Google Cloud SecOps remain important systems of record. SOAR platforms help automate repeatable workflows. XDR and CNAPP tools improve visibility across endpoints, cloud resources, identities, and applications. MDR providers add operational capacity. AI copilots help analysts move faster.
But the gap is between faster tools and a truly scaled operating model. That is where https://t.co/sMBpdWYtUL’s framing is interesting. Their Telemetry, Alert Screen, and Threat Loop approach is aimed at the full cycle: collect more of the evidence, screen alerts with evidence-backed AI verdicts, and feed analyst decisions back into detections and response logic. Just as important, they keep the governance model intact: agents do the work, people own the calls.
That human-led model matters. Full autonomy sounds impressive in a demo, but production security is about accountability. If a model changes, if a verdict is wrong, if a response action affects the business, someone needs to understand what happened and why.
That is the right problem to solve.
Are You Interested in Learning how to apply Artificial Intelligence to Cybersecurity (you should be)?
We have over 30 tutorials covering this subject and more coming!
https://t.co/P7a9GOPkcl
🤖 Can you really trust AI when it can confidently tell you something that's completely wrong?
Artificial intelligence is becoming an everyday part of how we work, research, learn and make decisions.
But there's still one major problem we can't afford to ignore: AI hallucinations. 🧠
AI tools such as ChatGPT can sometimes generate information that sounds incredibly convincing but is inaccurate, misleading or entirely fabricated.
And the most worrying part? They don't always make it obvious when they're wrong.
⚠️ AI can invent facts, statistics and references.
⚠️ It can provide incorrect answers with complete confidence.
⚠️ It can misunderstand context and fill gaps with plausible-sounding information.
As we increasingly rely on AI for business decisions, research, financial analysis and professional advice, these mistakes can have serious consequences.
But does that mean we shouldn't trust AI?
Not necessarily.
The key is understanding that AI is a powerful assistant, not an unquestionable authority.
We need to challenge its answers, verify important information and maintain our own critical thinking.
The future of AI isn't just about making systems more intelligent. It's about making them more reliable, transparent and trustworthy. 🚀
💬 Have you ever caught ChatGPT or another AI tool confidently giving you a completely wrong answer? 👇
#AI #ArtificialIntelligence #AIHallucinations #ChatGPT #GenerativeAI #AITrust #ResponsibleAI #CriticalThinking #AITips #FutureOfAI #AIAccuracy #Technology
Cybersecurity is a shared responsibility. In our recent discussion, Acting Director Nick Andersen emphasized the importance of collaboration across the cybersecurity community to address super intelligence-enabled cyber risks. Watch the full video: https://t.co/pxhHqV8D9m
AI that answers questions is one thing. AI that acts in the physical world is another.
At Gartner IT® Symposium/Xpo™, I'm exploring what enterprise networks must support when AI moves beyond the screen.
A thread with @TMobileBusiness.
T-Mobile For Business Partner
We’ve updated the cyber incident reporting form to help us better understand the scope + impact of incidents & prioritize response actions to support reporting orgs. Learn more👉https://t.co/MmFBshWfzg
Are You Ready to Start a Career in Cybersecurity?
This is the place to Start!
Getting Started Becoming a Master Hacker
#masterhacker#gettingstarted#cyberwarrior
https://t.co/GZm5Pbx1DJ
PwC’s USD 100 Million Reminder: Humans Still Need to Think
PwC is reportedly spending more than USD 100 million to certify 85,000 employees in critical thinking because AI is now doing more client work and, apparently, someone noticed that “the chatbot said so” is not a control framework.
Article: https://t.co/mPylY2Wojx
I don’t hate the idea of teaching people to challenge AI-generated work. In fact, I’ve been saying for years that AI is only as useful as the humans supervising it. But there is something both funny and slightly terrifying about a major professional services firm needing a formal certification to remind consultants and auditors to verify sources, question assumptions, and not outsource judgment to a probabilistic text machine with confidence issues.
This is the kind of thing we’re going to see a lot over the next few years. Companies rushed into AI expecting magic. What they got was a very fast intern who occasionally invents citations, misunderstands context, and says everything with the confidence of a senior partner on three espressos.
Now comes the hangover phase. Governance programs. AI review boards. Critical thinking badges. “Human-in-the-loop” committees. Maybe even a laminated desk card that says, “Did you check whether this is nonsense?”
The uncomfortable truth is that AI has limitations. It’s useful, powerful, and improving, but it is not a substitute for expertise, accountability, or basic professional skepticism. If an organization had weak judgment before AI, automation just helps it make bad decisions faster and at scale.
Before companies hand everything to AI, they need to dial in their own brains first. That means better data discipline, clearer decision rights, stronger architecture, real accountability, and people who understand enough about the business to know when the machine is bluffing.
So yes, train people to think critically. But let’s not pretend the certificate is the solution. The solution is a culture where checking the work matters more than looking innovative in the quarterly update.
Where Will The World Store Its Fast Growing Volume Of Data?
by @ChuckDBrooks
The AI revolution is driving unprecedented global demand for data storage prompting the exploration of new storage methods
https://t.co/Y1Q3nEeYkf
#data#tech#security#future