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WEBINAR: How To Use Artificial Intelligence To Prevent Insider Threats

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Interset CTO Stephan Jou joins Holger Schulze, CEO at Cybersecurity Insiders, to discuss the impact of insider attacks and how AI can be used to mitigate these threats. To watch the webinar recording, click here: https://register.gotowebinar.com/register/2916777136713869315

Want to learn more about the risks of insider threats? Check out highlights from the 2018 Insider Threat Report: https://www.slideshare.net/Interset/2018-insider-threat-report-infographic

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WEBINAR: How To Use Artificial Intelligence To Prevent Insider Threats

  1. 1. 1 | © 2018 Interset Software How To Use Artificial Intelligence To Prevent Insider Threats
  2. 2. 2 | © 2018 Interset Software Today’s Webinar Hosts Stephan Jou CTO, Interset Holger Schulze, CEO Cybersecurity Insiders
  3. 3. 3 | © 2018 Interset Software Impact Of Insider Attacks
  4. 4. 4 | © 2018 Interset Software Barriers To Insider Threat Management
  5. 5. 5 | © 2018 Interset Software Almost All Cybersecurity Problems Become Inside(r) Threats Unauthorized User Malware Phishing Ransomware CISO Security Architect Security Practitioner • Low Risk Visibility • Slow Threat Detection • Increasing Security Spend • Reduced SOC Efficiency • Security Tool Integration • Scalability, Interoperability • Analyst Efficiency • Alert Fatigue • Alert Triage • Threat Hunting & Investigation • Attack Mitigation
  6. 6. 6 | © 2018 Interset Software Unauthorized User Malware Phishing Ransomware But, Threats Are Obscured By Too Much Data, Too Many Systems CISO Security Architect Security Practitioner • Low Risk Visibility • Slow Threat Detection • Increasing Security Spend • Reduced SOC Efficiency • Security Tool Integration • Scalability, Interoperability • Analyst Efficiency • Alert Fatigue • Alert Triage • Threat Hunting & Investigation • Attack Mitigation Perimeter Network Servers Apps Users Data
  7. 7. 7 | © 2018 Interset Software Who Wants This Type Of Risk Visibility?
  8. 8. 8 | © 2018 Interset Software Current Security Tools Are Limiting • Rules & Thresholds Based • Fragmented • Inefficient • Reactive • Scattered • Overwhelming • 60–80% false positives • Not enough data for visibility • Not enough staff
  9. 9. 9 | © 2018 Interset Software Where Companies Want To Use AI 34% of companies plan/are using AI to mitigate security risks
  10. 10. 10 | © 2018 Interset Software Need AI To Automate And Scale To Risk “By 2020, 60% of digital businesses will suffer major service failures due to IT security teams’ inability to manage digital risk” Gartner
  11. 11. 11 | © 2018 Interset Software DEFINING AI
  12. 12. 12 | © 2018 Interset Software Artificial Intelligence Input Processing Output Learning Decision & Inference Knowledge & Memory Knowledge Representation, Ontologies, Graph Databases, … Prescriptive Analytics, Optimization, Decision Making, … Machine Learning (supervised, unsupervised) NLP Speech Recognition Visual Recognition … Data Sources Robotics, Navigation Systems Speech Generation Threat Leads
  13. 13. 13 | © 2018 Interset Software Different Types Of Machine Learning Source: MathWorks Deep Learning Learning by example Learning by pattern discovery
  14. 14. 14 | © 2018 Interset Software § Based on ideas started in 1940’s § Biologically inspired “Neurons” § Input on the left, output on the right Learning = § Examples compared to actual output § Differences used to modify the weights (strength of connections) § Iterate Input Output 1980’s: Neural Networks
  15. 15. 15 | © 2018 Interset Software Use a neural network to discriminate between tanks and trees Data § 200 pictures (100 tanks, 100 trees) Compute § One 1980’s mainframe Results § Suboptimal :-) 1980’s: Pentagon & Tanks
  16. 16. 16 | © 2018 Interset Software 1M x cycles (Hz) More Compute 33,000 x pixels More Data Convolutional, Feedforward, Adversarial LSTM, Ensemble Better Algorithms Government, Universities, Startups, Big Companies Broad Investment * According to Andreessen Horowitz What’s Different Now?
  17. 17. 17 | © 2018 Interset Software AI FOR INSIDE(R) THREAT DETECTION
  18. 18. 18 | © 2018 Interset Software Insider Threat Detection Requires Measuring “Unique Normal” Current tools scalability shortcomings must assume common patterns/rules for entire population Comparing everyone to the same pattern means many false positives Measuring “Unique Normal” for each user/ machine/ filesystem /printer /.. results in accuracy Only large scale machine learning can measure what is normal for every user for every category
  19. 19. 19 | © 2018 Interset Software “Unique Normal”, Or Not Requires Big Data & Unsupervised #ML Supervised approaches, such as deep learning, is good for cybersecurity data with lots of labels, i.e. malware. The malware use case has decades’ worth of example binaries, both malicious and innocent. Unsupervised approaches are best for cybersecurity data with limited data, typically without labels, such as detecting anomalies indicative of unique insider threats where there is not enough data for supervised ML. Supervised learning is learning by example and requires “labeled” data. Unsupervised learning is self-discovery of patterns and doesn’t need labels/examples.
  20. 20. 20 | © 2018 Interset Software Unsupervised Machine Learning Requires Big Data Compute Self-Learning Big Data Storage Big Data Compute Contextually Integrated Automated
  21. 21. 21 | © 2018 Interset Software Platform Based On Unsupervised Machine Learning & AI ACQUIRE DATA HIGH QUALITY THREAT LEADS INTERNAL RECON INFECTED HOST DATA STAGING & THEFT COMPROMISED ACCOUNT LATERAL MOVEMENT ACCOUNT MISUSE CUSTOM FRAUD DLP ENDPOINT Biz Apps CUSTOM DATA NETWORK IAM Kibana DETECT, MEASURE AND SCORE ANOMALIES CREATE UNIQUE BASELINES Contextual views. Drill-down and cyber-hunting. Broad data collection Determine what is normal Gather the raw materials Find the behavior that matters Workflow engine for incident response.
  22. 22. 22 | © 2018 Interset Software Measuring Unique Normal Enables Accurate Anomaly Detection Data Repository Logs Active Directory Logs VPN Logs Feature Extraction Ann moves a significant volume of data Ann access and takes from file folders Ann accesses anomalous repositories Ann logs in from anomalous location Ann logs in at unusual time of day (other features) (other features) (other features) 𝑝" 𝑝# 𝑝$ ∑ 𝑝% 𝑝& 𝑤" 𝑤# 𝑤$ 𝑤% 𝑤& Anomaly Detection Auth./Access Anomaly Model File Access & Usage Models Volumetric Models VPN Anomaly Models Entity Risk Aggregation Entities - Account - Machine - File - Application 96
  23. 23. 23 | © 2018 Interset Software AI Transforms Existing Security Data Into Threat Leads
  24. 24. 24 | © 2018 Interset Software Here, Interset distills more than 5.1 billion events into 1 million anomalies, for 29 validated and prioritized threat leads Anomalies Detected By AI Are Surfaced In The User Interface ”Unique Normal” measured for 12K users, 12.8K machines, 2.4M files, 632 projects, 59 servers, 104 shares, 82 resources, 1.37M websites, 12K IP addresses. Enterprise risk score aggregated across all individual entities’ “unique normal” (or not!) measurements.
  25. 25. 25 | © 2018 Interset Software Ex. Data Exfiltration via Email Anomaly Detection (e.g. Proofpoint) 960 GB of email data per hour was observed at 3-4 am, higher than any personal or population norm Yaman has a norm of 1.5 kB of email/hr Yaman has a high of 2.4 kB of email/hr 1 Avg of 14.8 kB of email/hr for all pop. 690 MB/hr of email is expected high for all pop. 2 3
  26. 26. 26 | © 2018 Interset Software Ex: Insider Fraud Detection via Expense Reporting Anomalies 18 entertainment claims in a week - higher than any norm Norm of 6 for D Larkin High of 4 for D Larkin Avg 1.5 for all users High 16 for all users 1 2 3
  27. 27. 27 | © 2018 Interset Software Ex: Using Anomalies To Distinguish Humans From Bots TCP/465 on this machine is not a human activity This particular machine doesn’t normally have humans using it at 4am 1 2 Click to Investigate Potential Infected Host 3
  28. 28. 28 | © 2018 Interset Software Unauthorized User Malware Phishing Ransomware AI Surfaces The Insider Threats Hidden By All The Noise CISO Security Architect Security Practitioner • Accelerated Threat Detection • Expanded Risk Visibility • Increased SOC Efficiency • Optimize Security Investments • Augment Security Tools • Integrated Risk Visibility • Noise-Cancelling Analytics • Integrated Platform • Faster, Focused Threat Hunting • Accelerated Alert Triage • Guided Investigation • Detect Multi-faceted Attacks
  29. 29. 29 | © 2018 Interset Software AI Enables Automated Trace And Investigation Of Insider Threats
  30. 30. 30 | © 2018 Interset Software About Interset.AI SECURITY ANALYTICS LEADER PARTNERSABOUT US Data science & analytics focused on cybersecurity 100 person-years of security analytics and anomaly detection R&D Offices in Ottawa, Canada; Newport Beach, CA Interset.AI
  31. 31. 31 | © 2018 Interset Software QUESTIONS? INTERSET.AI

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