Deception Technology in AI Security Strategy

The most dangerous assumption about AI in security may be that more analysis automatically means more visibility. Security teams already collect enormous amounts of telemetry. Email events, identity activity, endpoint alerts, network data, cloud logs, and threat intelligence can produce more information than analysts can realistically investigate. AI changes the economics of processing that information. It can identify patterns and correlations humans would struggle to find manually. But the quality of the outcome still depends on the quality and completeness of what the system can see. If identity telemetry is missing, email events are isolated from endpoint activity, or important business context never reaches the analysis layer, AI may simply become extremely efficient at analyzing an incomplete picture. The governance question for CIOs is therefore bigger than "Where can we deploy AI?" Which missing source of context creates the greatest risk of giving leadership a false sense of security? https://lnkd.in/esTeKJv4 #CyberSecurity #SecurityLeadership #ThreatDetection #RiskManagement

AI is changing cybersecurity faster than many organizations can adapt. In conversations with customers and partners, I increasingly see the same challenge: security teams have more data, more alerts, and more vulnerabilities than they can realistically manage manually. This is where AI and Machine Learning can make a real difference — not by replacing security professionals, but by helping them prioritize better, detect faster, and focus on the risks that actually matter. From anomaly detection and vulnerability prioritization using CVSS + EPSS to continuous compliance and cloud security monitoring, AI is becoming an important part of modern security operations. At the same time, we should not underestimate the challenges around data quality, governance, explainability, and human oversight. At Secrails, we believe the future is about combining AI + automation + continuous cloud visibility + human expertise. We explore this in more detail in our latest article https://lnkd.in/dmxazPF8 #Cybersecurity #ArtificialIntelligence #MachineLearning #CloudSecurity #AISecurity #CSPM #Secrails

🚨 YOUR AI AGENT MAY HAVE MORE ACCESS THAN YOUR EMPLOYEES. AI agents are becoming capable of: 🤖 Reading data ☁️ Accessing cloud resources 🔗 Calling APIs ⚙️ Executing workflows 🔐 Using credentials That creates a new cybersecurity question: What happens when an AI agent is compromised? A traditional user may access one system. An AI agent connected to multiple services could potentially move across an entire workflow. 🛡️ Organizations should consider: • Least-privilege permissions for AI agents • Separate credentials for every agent • Short-lived access tokens • Continuous activity monitoring • API-level controls • Human approval for high-risk actions AI security isn't only about protecting the model. It's about controlling what the model is allowed to do. 💡 Give AI more autonomy? Give it less privilege. Would you allow an AI agent unrestricted access to your cloud environment? 👇 Yes or No? #AIsecurity #CyberSecurity #CloudSecurity #AI #ZeroTrust #CloudComputing #ThreatIntelligence #NetSentinelAI

𝗗𝗮𝗶𝗹𝘆 𝗧𝗲𝗰𝗵 𝗨𝗽𝗱𝗮𝘁𝗲 • Google’s New Gemini-Powered AI Voice Features Come to Gmail, Docs, and Keep - https://lnkd.in/d-ZmERcv • Abnormal AI Brings OpenAI Daybreak Models Into AI Cloud Security to Protect Against Rogue AI - https://lnkd.in/dPY9MsmA • Two in Five Healthcare Organizations Are Building with AI Without a Developer, Paubox Finds - https://lnkd.in/dRDujyDw • Elastic Brings OpenAI GPT Cyber Models Into Elastic Security to Help Defenders Investigate and Remediate Threats Faster - https://lnkd.in/dpxijAgx #AI #Cybersecurity #OpenAI #Google #VoiceAI #TechNews #SecureITWorld

AI is changing the economics of cyberattacks. But there’s another problem emerging underneath the AI arms race: data access. As attackers increasingly operate at machine speed, defensive AI needs to do the same. That means being able to reach the evidence. Not just today's alerts, but months or years of identity events, endpoint telemetry, network activity, cloud logs and security history. At enterprise scale that is already a petabyte problem. Across the broader security ecosystem it becomes an exabyte-scale data challenge. In our latest blog, Craxel's CEO and founder David Enga explores why traditional approaches to storing and searching that data could become a fundamental constraint on AI-powered cybersecurity. If AI is going to fight AI, the infrastructure underneath it has to keep up. Read the full blog: https://lnkd.in/eE6kUd3Y #Cybersecurity #AI #DataInfrastructure #SecurityAI #BlackForest

So many screaming headlines about AI this week. Yesterday, OpenAI disclosed several examples of models behaving in unexpected ways during testing. One found and used an exposed API key without permission. Another uploaded a file to the internet without being asked. WOW. 🥺 I’ve been spending a lot of time thinking about the policy behind making AI safer, and I keep coming back to something pretty simple: We basically need a black box for AI. At Human Aligned.org, we’re asking a simple question: Can we build the safeguards for humanity as fast as we’re building the models? So, how DO we do this? As these systems become capable of taking actions, we need to be able to see what happened, investigate failures and learn from them. That means building the systems around the model - monitoring, security, reporting, independent investigation, standards and critically >> accountability. A technical piece from the folks at Craxel this week is a good example of one piece of that infrastructure: making sure we can actually access the evidence when something goes wrong.

Great article on the scale, speed and cost challenge defensive AI is creating for enterprise cyber. Machine-speed attacks will require machine-speed defense. But giving AI instant access to months or years of security data gets expensive fast. So today we brute-force the problem with massive amounts of compute, while rationing the data we keep immediately accessible by shortening retention, sampling, aggregating, or moving older data to cold storage. Then we ask AI to identify or reconstruct an attack, at machine speed, from an incomplete case file. That’s a problem we’re working with our customers to get ahead of with Black Forest. Check out David Enga's blog here. https://lnkd.in/eE6kUd3Y

Our portfolio company Craxel has published a sharp piece on a defining challenge in AI-era cybersecurity. As attackers start operating at machine speed, defenders have to as well. AI is well suited to the job, but it can only reason over evidence it can actually reach. And that evidence is rarely in a single alert. It is spread across months of identity, endpoint, network and authentication data that is often too expensive to keep instantly searchable. Craxel's point: the question is no longer just how intelligent your AI is, but how much of your environment it can actually see, and how fast. That is exactly what their Black Forest platform was built to solve, and a big part of why we backed them. David Enga Gary Connolly Gerry Buggy Mike Powell Kevin McGivern Well worth a read, link in comments 👇 #Cybersecurity #AI #DataInfrastructure #SecurityAI #BlackForest

Been thinking a lot about AI lately and how fast companies are starting to implement it into their everyday operations. There’s obviously a ton of upside, but I think companies also have to be very aware of what they’re actually giving these AI systems access to.... Once AI starts interacting with internal data, cloud resources, APIs, applications, and business processes, it’s not just an AI conversation anymore. It becomes a serious security conversation too that I think is being overlooked. What data does it have access to? Can it be manipulated into doing something it shouldn't? How is it's identity being managed? Are you giving it more permissions then it needs? If something does go wrong do we have the visibility to see what happened? Truth is AI isn't going anywhere and it's only going to become more capable, which makes securing the environment around it just as important as the AI itself. Companies are going to keep adopting AI either way. I think the bigger question is whether security can keep up with how quickly we’re giving these systems more access and responsibility. #AISecurity #CloudSecurity #Cybersecurity #AI #CloudEngineering

Every day, sensitive data quietly leaves the building, through a prompt, not a hacker. The biggest data leak today may not come from a cyberattack, it can happen during an ordinary workday. And this is exactly why the conversation around AI is changing. It's no longer "Should we adopt AI?" It's "How do we protect our data while using AI at scale?" That's why Data Loss Prevention (DLP) is making a comeback. AI changes how we work. DLP changes how we protect it. To ensure sensitive data stays protected wherever it moves, across people, applications, cloud platforms, and AI tools. The organizations that win, won't be the ones with the smartest models. They'll be the ones who protected their data well enough to use those models safely. #PrudentTechnologiesAndConsulting #CyberSecurity #AI #DLP #DataProtection