Intelligence-Driven Security
Modern adversaries use automation, AI-generated phishing, and polymorphic malware to evade traditional signature-based defences. Staying ahead requires security systems that learn, adapt, and respond at machine speed — augmenting human analysts rather than replacing them.
Daniel Ossio designs and implements AI-powered security ecosystems that transform raw telemetry into actionable intelligence. With 25+ years of understanding how attacks evolve, Daniel ensures AI tools are calibrated to detect real threats — not drown analysts in false positives.
AI Security Services
ML Threat Detection
Deploying machine learning models that identify anomalous behaviour, detect unknown malware variants, and flag emerging attack patterns across network, endpoint, and cloud telemetry.
Behavioural Analytics
User and Entity Behaviour Analytics (UEBA) that baseline normal activity and detect insider threats, compromised accounts, and lateral movement through statistical modelling.
Threat Intelligence
Operationalising threat intelligence feeds: IOC enrichment, threat actor profiling, attack surface monitoring, and integration with SIEM/SOAR platforms for automated response.
Automated Threat Hunting
Proactive threat hunting programmes powered by AI-assisted hypothesis generation. Combining human expertise with machine-speed data correlation to find hidden adversaries.
AI-Powered SIEM & SOAR
Traditional SIEM deployments are drowning in data and generating alert fatigue. Daniel designs next-generation SIEM architectures that leverage AI to:
- Reduce noise: ML-driven alert prioritisation that surfaces genuine threats and suppresses false positives
- Correlate at scale: Cross-source correlation across cloud, network, endpoint, and identity telemetry
- Automate response: SOAR playbooks that contain threats in seconds, not hours — with human approval gates
- Predict attacks: Predictive models that identify emerging threat patterns before exploitation occurs
- Measure effectiveness: Detection engineering metrics that quantify security posture and coverage gaps
Securing AI Systems
As organisations adopt AI and LLMs, new attack surfaces emerge: prompt injection, model poisoning, training data extraction, and adversarial inputs. Daniel provides guidance on securing AI deployments — ensuring your AI systems are resilient against manipulation while delivering their intended business value.