AI-Driven Surveillance & Cybersecurity

I want to add an AI layer that watches over both my physical premises and my network in real time. On the surveillance side, the system must accept video and audio feeds, then automatically flag unusual activity—anything outside of learned patterns—so I can respond before a threat escalates. No face recognition or classic perimeter-only intrusion rules; the priority is behavioural anomaly detection that keeps improving as more data comes in. At the same time, I need the same intelligence applied to my cybersecurity stack: log streams, traffic flows, and endpoint data should be analysed with the same anomaly-detection engine so I receive a single, consolidated alert panel instead of siloed dashboards. A clean API, preferably REST or gRPC, is essential so the solution can plug into my existing VMS and SIEM. Python and TensorFlow or PyTorch are fine, but I’m open to purpose-built platforms if they shorten deployment without locking me in. Deliverables • Model(s) trained for video + audio unusual-activity detection • Integration scripts / middleware connecting models to current cameras, mics, and SIEM • A lightweight dashboard (web or Electron) showing live alerts and confidence scores • Deployment guide and basic retraining instructions Acceptance criteria: live demo on my sample feeds, under 2 s alert latency, plus a one-week log run with less than 5 % false positives.