Forrester study: Relativity DBR boosts ROI by 143% and saves $10M

Impressive results from the Forrester TEI study! Exciting to see how Relativity #DataBreachResponse is driving measurable impact for organisations from reducing manual effort to improving overall response efficiency.

💡From data to decisions - How GenAI and DPI are shaping the future of network management Are you struggling to make GenAI truly work for your network operations? ➡️ Our new whitepaper provides a comprehensive exploration of how DPI unlocks the power of GenAI for network management. Learn how to leverage DPI’s granular traffic intelligence for model training, validation, threat detection, and optimization. Key learnings: ✅ Understand the critical DPI metrics needed for successful GenAI implementation ✅ Explore real-world use cases: GenAI-powered load balancing and CASBs ✅ Future-proof your network with DPI’s advanced capabilities for evolving GenAI models 👉 Download free whitepaper [Link in the comments]

If you mute the sound on the American healthcare system and only watch the structure, The patterns match the exact behavioral architecture of every documented predatory network we’ve ever exposed: Secrecy, dependency creation, financial entrapment, power consolidation, gatekeeping, and a hierarchy designed so no one in the system can safely whistleblow. It’s not that “healthcare is a cult.” It’s that the architecture is identical to the models that are legally recognized as coercive, exploitative, or criminal. And once you see the patterns-only version, you can’t unsee it.

🔹 Post 2 of 5: Real Voices. Real Results. Efficiency isn’t just a buzzword, it’s a business advantage. Glen McFarlane describes how DBR empowers his team to work more efficiently and cost-effectively, transforming how they deliver results. 🎥 Watch the video below. Missed yesterday’s kickoff? Catch it here → https://lnkd.in/g7E5QnwH #WorkflowWins #DBR #WhyWeChoose Relativity Kroll #RespondsResponsibly

Determining Critical Success Factors of the Digital Transformation Using a Force-Directed Network Graph Gianni Pasqual, Jürgen Jung, Bardo Fraunholz How to Cite: Pasqual, G., Jung, J., & Fraunholz, B. (2023). Determining Critical Success Factors of the Digital Transformation Using a Force-Directed Network Graph. Complex Systems Informatics and Modeling Quarterly, 37, 22-53. https://lnkd.in/dZAJJKX5 https://lnkd.in/dftX-gBH #DigitalTransformation, #CriticalSuccessFactors

Why does variational inference (VI) reliably underestimate uncertainty compared to Hamiltonian Monte Carlo (HMC)? For tech leaders relying on predictive models, missing uncertainty can tank performance—especially in real-world cases like Bayesian logistic regression. VI often assumes variables are independent (mean-field), and KL(q‖p)'s mode-seeking direction misses outlier probabilities. HMC, though slower, captures richer uncertainty. The trade-off: faster answers vs. authentic uncertainty—choose wisely. We’ve seen teams move from VI to HMC for critical predictions—gaining confidence, but paying with compute. 💡 Want a visual comparison of VI vs. HMC workflows? Comment “Posterior” and I’ll send it.

❓Go from 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 to 𝙤𝙥𝙥𝙤𝙧𝙩𝙪𝙣𝙞𝙩𝙮! ✨ Large-scale industries face high variability, complex constraints, and costly inefficiencies when traditional models fail to keep up with real-time decision-making. In this blog, we look at SourceOne®'s capabilities in the following areas: ✴️Optimization  ✴️Simulation  ✴️Stochastic Optimization ✴️Reinforcement Learning Click here to read the full paper: [link https://lnkd.in/gqJCn2p9] #SourceOneEKPS #StochasticOptimization #RL #Simulation

LLMs alone are not AGI. An LLM serves as a repository for reusable vector programs, acquired through gradient descent on human data. The defining characteristic of general intelligence lies in the efficiency of skill acquisition—how effectively information is extracted from experience and transformed into programs that generalize well. Gradient descent, as used in LLMs, is significantly less efficient than human intelligence in this aspect. LLMs may be a component of AGI, perhaps as a memory or knowledge representation tool, but they are not the complete solution. #LLM #AGI #ArtificialIntelligence #MachineLearning #TechInsights

We often see “human error” in the headlines following a failure of some kind - but is this really the whole story? Labelling a system failure's primary cause as human error makes sense! A person accidentally pressed a button they shouldn’t have and we can stop them from pressing that button next time! It’s simple, it’s neat, we move on. But whose fault is it, really? Was it the person’s for pressing the button (because surely the system was working perfectly before that)? Or was it the technology’s for having an accidentally pressable button (so we can’t fault the poor, clumsy, human!)? Human error v’s technology failure is a false dichotomy that is prevalent throughout technology and reinforced in media reporting. It oversimplifies the invariable complexity that exists in the interactions between humans and technology. It acts as a distraction from truly understanding the root cause of a system failure. To move past this dichotomy, we need to explore the human-technology interactions of a system; how system conditions and design can mitigate or exacerbate potential issues; and how human errors and technology failures are likely symptoms of a deeper and more complex issue. Please note: this post wasn’t inspired by me accidentally pressing a button that I shouldn’t have.

Addressing complex and large incidents requires solutions that balance speed with precision. These results demonstrate the measurable value of a responsible and data driven approach to breach response.