Data　flow　learning　algorithms　must　be　very　efficient　in　learning　and　predicting　sequences.　The　model　that　monitors　a　sequence　of　data　or　events　can　predict　the　sequel　and　can　act　in　such　a　way　that　it　optimally　achieves　the　desired　result.　Security　and　digital　risk　tracking　systems　are　receiving　a　constant　and　unlimited　input　of　observations.　These　data　flows　are　characterized　by　high　variability,　as　their　properties　can　change　drastically　and　unpredictably　over　time.　Each　incoming　example　can　only　be　processed　once,　or　it　must　be　summarized　with　a　small　memory　imprint.　This　research　paper　proposes　the　development　of　an　intelligent　system,　for　real-time　detection　of　data　flow　anomalies　related　to　information　systems＇　security.　Specifically,　it　describes　the　implementation　of　an　efficient　and　high-precision　energy-based　Online　Sequential　Extreme　Learning　Machine　(e-b　OSELM)　that　is　proposed　for　the　first　time　in　the　literature.　It　is　an　intelligent　model　that　can　detect　data　dependencies,　by　applying　a　measure　of　compatibility　(scalable　energy)　to　each　configuration　of　its　variables.　It　assigns　low　energy　to　the　correct　values　and　higher　energy　to　the　divergent　(abnormal)　ones.　The　innovative　combination　of　energy　models　and　ELMs　offers　high　learning　speed,　ease　of　execution,　minimum　human　involvement　and　minimum　computational　power　and　resources　for　anomaly　detection　and　identification.