Cybersecurity in the AI Era: Defend, Attack (OSCP+) & Govern

Explore prompt engineering for alert summarization in generative ai to accelerate incident triage and automated response by condensing dense alerts into quick, actionable summaries.

Leverage large language models to conceptually correlate alerts from diverse tools, revealing attack chains without strict field matching. Map observed behaviors to MITRE ATT&CK and gain actionable, human-readable incident context.

Discover how generative AI creates dynamic, context-aware playbook suggestions for incident response, translating incident narratives into tailored containment, investigation, eradication, recovery, and post-incident activities.

Learn how agentic AI workflows on platforms like Radiant Security automate security operations from observe through plan and execute to escalate, with governance and human-in-the-loop checks reducing triage time.

Observe a high-severity alert and reason through hypotheses in an auditable agentic triage loop—observe, reason, plan, execute, reflect—guided by MITRE techniques and encoded payload indicators.

Explore Stellar Cyber's agentic investigation flow, from alert ingestion to automated execution and reflection, leveraging a unified data lake, large-language model-powered reasoning, MITRE ATT&CK mapping, and governed autonomy.

Lower mean time to respond from four hours twelve minutes to 38 minutes by deploying agentic ai for alert summarization, correlation, enrichment, and auto actions with governance.

Empower analysts with agentic AI assistants to drastically reduce repetitive tasks, accelerate decisions, improve consistency, and amplify human skills, delivering faster containment and lower burnout.

Explore the limitations and risks of automated response, including false positives, over-automation, agentic hallucination, adversarial evasion, model drift, and governance requirements, with mitigation strategies and human oversight.

Embed artificial intelligence as a natural upgrade to daily blue team workflows, delivering overnight summaries, AI-prioritized incidents, and one-click actions that streamline triage, investigation, and response.

Explore how agentic ai powers a multi-agent security operations center, handling 80 to 90 percent of routine and semi-routine alerts from detection through remediation, with humans reviewing summaries or exceptions.

Explore how UEBA, or user and entity behavior analytics, evolves in the AI era to detect insider threats and compromised accounts through self-learning models, deep contextual sequencing, and graph-based relations.

Discover clustering techniques for user and entity behavior baselines, including k-means, dscan, hierarchical clustering, and Gaussian mixture models, to identify normal patterns and flag anomalies in unsupervised learning.

Detect deviations from learned normal behavior using time series analysis. Apply exponential smoothing, ARIMA, PROFIT, and deep models to capture trend, seasonality, and anomalies.

AI builds probabilistic user profiles from streaming telemetry using temporal, spatial, volumetric, and sequential features with clustering and time-series models. It continuously updates and generates natural language descriptions of profiles.

Learn how conceptual flagging uses dynamic profiles, time-series forecasts, and multi-dimensional deviations—magnitude, velocity, rarity, sequence, and context—to produce risk scores and human-in-the-loop decisions for insider threats.

Real-world UEBA successes demonstrate how adaptive machine learning and contextual reasoning detect insider threats, privileged account abuse, and ransomware precursors, enabling rapid containment and preventing data breaches.

Leverage user and entity behavior analytics to detect sophisticated insider risks that evade signatures, uncover low and slow attacks, and provide early warning in the AI era.

Examine the limitations of behavioral artificial intelligence models, including false positives, cold starts, drift, and privacy concerns. Emphasize governance, human-in-the-loop, and drift monitoring to maintain effective, trustworthy analytics.

Orchestrate UEBA with EDR, NDR, identity threat detection and response, deception technologies, threat intelligence, and generative AI to build a layered defense that reduces false positives and speeds containment.

Consolidate behavioral analytics and insider threat modeling with artificial intelligence to deliver prioritized recommendations for security teams, including piloting techniques, dynamic per-user profiles, layered controls, and human-in-the-loop governance.

Explore how AI-enhanced endpoint detection and response uses continuous behavioral modeling, multi-paradigm machine learning, and real-time behavioral blocking to stop fileless and living-off-the-land threats at execution.

Extended detection and response platforms unify telemetry across endpoints, networks, and cloud into a data lake, enabling holistic visibility, cross-domain analytics, AI-driven prioritization, and integrated investigation and response.

Use ai-driven predictive remediation to forecast attack progression from current observables and correlation, and implement prioritized containment and hardening actions before adversaries reach high-value targets.

Forecasts weaponization likelihood of vulnerabilities with AI to prioritize patching, detection tuning, and hardening before attacks. Uses time series, NLP, graph modeling, and ensemble scoring for a 30–90 day list.

Leverage predictive models to proactively prioritize remediation by combining exploit forecasts with asset criticality and reachability into a dynamic, risk-based remediation roadmap.

Compare Sentinel-1 and CrowdStrike's AI-native endpoint defense, featuring real-time behavioral analysis, autonomous prevention, predictive threat models, generative analyst assistance, and unified cross-domain visibility with human governance.

This lecture explains the shift from reactive to predictive defense using AI and telemetry to forecast attack paths and harden targets preemptively.

Explore how artificial intelligence compresses dwell time from weeks to hours by using behavioral blocking, endpoint detection and response, and predictive models to prevent lateral movement and rapid containment.

Explore how predictive and behavioral defense raises ethics and privacy questions in endpoint, network, and cloud monitoring. Implement governance with privacy by design, data minimization, and transparent human oversight.

Adopt phased, low-risk integration for enterprise networks. Map the stack, normalize data to a schema, and enable real-time connectivity, orchestration, and governance across EDR, SIEM, cloud logs, and identity systems.

Shift from reactive to predictive defense with AI-enabled detection and forecasting. Build layered, governed defenses—endpoint AI, EDR, behavior analytics, and predictive remediation—to reduce dwell time.

Explore offensive security with artificial intelligence under strict authorization, scoping, and ethics. Apply module-based practices for reconnaissance, phishing simulations, AI-assisted testing, and red teaming to strengthen defenses.