Salt Lake City DevOpsDays

A talk about security and DevOps and the Assimilation project.

Transcript

1. Security Automation for DevOps
   Security Automation for DevOps
   #AssimProj @OSSAlanR
   Alan Robertson
   Assimilation Systems Limited
   http://AssimilationSystems.com
   

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   Biography
   Biography
   ●
   35+ years in IT/development – 10 years in system
   management (SysAdmin)
   ●
   Founded Linux-HA project - led 1998-2007 – aka “Heartbeat” -
   now called Pacemaker
   ●
   Founded Assimilation Project in 2010
   ●
   Founded Assimilation Systems Limited in 2013
   ●
   Alumnus of Bell Labs, SuSE, IBM
   

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   Security questions
   Security questions
   ●
   Do you think good security staff is easily available?
   ●
   Do you think security is going to get better soon?
   ●
   Do you think you have enough staff for security to keep up
   with changes at DevOps / Agile rates?
   

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   Disturbing Trends...
   Disturbing Trends...
   30% of break-ins come through “lost” systems (Verizon)
   90% have had failures of unmonitored services (Turnbull)
   71% are unable to stay in compliance (Verizon)
   30% only start monitoring only after a problem (Turnbull)
   30% of systems doing nothing useful (Koomey)
   

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5. Highly Scalable Discovery-Driven
   Highly Scalable Discovery-Driven
   Automation
   Automation
   Continuous Discovery drives everything
   ●
   Continuous extensible discovery (CMDB)
   – systems, switches, services, dependencies – zero network footprint
   discovery process
   ●
   Extensible exception monitoring
   – more than 100K systems
   ●
   Discovery Drives Best Practice Analyses
   – Initially concentrating on security
   ●
   All data goes into central graph CMDB (Config Mgmt Data Base)
   

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   This all sounds unreasonable...
   This all sounds unreasonable...
   ●
   Huge scalability without complexity?
   ●
   Discovery without pings or port scans?
   Really?
   

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   Simple Scalability
   Simple Scalability
   I can explain how we scale so your
   grandmother would understand...
   

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8. Massive Scalability –
   Massive Scalability – or
   or
   “I see dead servers in
   “I see dead servers in O
   O(1) time”
   (1) time”
   ●
   Adding systems does not increase the monitoring work on any system
   ●
   Each server monitors 2 (or 4) neighbors
   ●
   Each server monitors and discovers its own services
   ●
   Ring repair and alerting is O(n)
   – but a very small amount of work
   Current Implementation
   

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   Minimizing Network Footprint
   Minimizing Network Footprint
   (in roadmap)
   (in roadmap)
   ●
   Support diagnosing switch issues
   ●
   Minimize network traffic
   ●
   Ideal for multi-site arrangements
   

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    Assimilation Architecture
    Assimilation Architecture
    ●
    Central Collective Management Authority
    – written in Python
    – delegates most work to nanoprobes – does nothing as much as possible
    – Doing nothing scaless really well – should be into the 100K system range
    ●
    Fully distributed “nanoprobe” agents
    – Simple, policy-free
    – Written in 'C'
    – Run scripts for monitoring or discovery
    – Send/receive heartbeats
    – Listen for ARP, LLDP, CDP packets
    ●
    Neo4j graph database
    

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    Assimilation Security Discovery
    Assimilation Security Discovery
    ●
    All IP+MAC Addresses on subnet
    ●
    Network Connectivity (CDP/LLDP)
    ●
    Packages and versions installed
    ●
    Services Offered / Used (netstat)
    ●
    Checksums of network-facing binaries, libraries, JARs
    ●
    Many other detailed security settings, permissions
    – (sshd_config, PAM, /proc/sys, auditd_conf, sudoers, etc)
    

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    Assimilation Analysis / Reports
    Assimilation Analysis / Reports
    ●
    Comparison to best practices
    ●
    Risk scoring
    ●
    Unknown IP Addresses
    ●
    Automatic service monitoring
    ●
    Unmonitored services
    ●
    Triage-related risk scores
    

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    B
    Best Practice Analyses
    est Practice Analyses
    ●
    Triggered by Discovery Updates
    – Analysis occurs within seconds of change
    – No change => No analysis
    ●
    We can analyze anything discovered
    ●
    You can easily discover anything you want
    ●
    Alerts and Reports available
    

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    Demo
    Demo
    Everything will be discovered – nothing will be configured manually
    ●
    What needs hardening
    ●
    How to Triage your hardening issues
    ●
    How to Demonstrate and Track Progress
    ●
    How to keep them in compliance (hardened)
    ●
    Visualizing Your Attack Surface
    ●
    Who has what package+version
    – Docker package discovery too!
    

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    Where to find this Information online?
    Where to find this Information online?
    http://assimilationsystems.com/category/getting-started/
    1. 15 Minutes To Better Security
    2. An Hour To Better Security
    3. A Half-Day To Better Security
    Where to See Similar Demos
    ●
    http://assimilationsystems.com/category/videos/
    ●
    http://assimilationsystems.com/sample-demo-output/
    

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    Future Plans
    Future Plans
    ●
    Checksum whitelist/blacklist ==> risk scores
    ●
    Checksum queries
    ●
    Create service consolidating vendor patches
    ==> risk scores
    ●
    Interactive User Interface(s) (GUI)
    

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    Get Involved!
    Get Involved!
    ●
    Get Assimilated!
    ●
    Contribute!
    – Testers, System Management, Continuous Integration
    – Best practice experts
    – Designers
    – Developers (C,Python, Shell, PowerShell, JavaScript)
    – Porters (esp Windows)
    – Promoters, Publicists, Packagers, etc.
    

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    Resistance Is Futile!
    Resistance Is Futile!
    These slides: bit.ly/DevOpsDaysRox16
    Mailing List: bit.ly/AssimML
    @OSSAlanR
    #assimilation on irc.freenode.net
    Project Web Site: assimproj.org
    Company Web Site: assimilationsystems.com
    Download: assimilationsystems.com/download
    

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    Switch Discovery Graph from LLDP / CDP
    Switch Discovery Graph from LLDP / CDP
    

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    Security Demo / HOWTO
    Security Demo / HOWTO
    ●
    No configuration was supplied
    – everything comes from discovery
    

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    Risk Management/Mitigation
    Risk Management/Mitigation
    ●
    Intrusions
    ●
    Vulnerable Software
    ●
    Licensed Software
    ●
    Audit Risk
    ●
    Outages
    ●
    System management
    

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    Why a graph database? (Neo4j)
    Why a graph database? (Neo4j)
    ●
    Humans describe systems as graphs
    ●
    Dependency & Discovery information: graph
    ●
    Speed of graph traversals depends on size of subgraph,
    not total graph size
    ●
    Root cause queries  graph traversals – notoriously slow in
    relational databases
    ●
    Visualization is Natural
    ●
    Schema-less design: good for constantly changing
    heterogeneous environment
    ●
    Graph Model === Object Model
    

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    Monitoring Pros and Cons
    Monitoring Pros and Cons
    Pros
    Simple & Scalable
    Uniform work distribution
    No single point of failure
    Distinguishes switch vs
    host failure
    Easy on LAN, WAN
    Multi-tenant approach
    Cons
    Active agents
    Potential slowness
    at power-on
    

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    Sixth Dimension: Graph Schema
    Sixth Dimension: Graph Schema
    Two Schema subgraphs
    ●
    Client / server
    dependency
    ●
    Switch interconnect
    

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    "sshd": {
    "exe": "/usr/sbin/sshd",
    "cmdline": [ "/usr/sbin/sshd", "-D" ],
    "uid": "root",
    "gid": "root",
    "cwd": "/",
    "listenaddrs": {
    "0.0.0.0:22": {
    "proto": "tcp",
    "addr": "0.0.0.0",
    "port": 22 },
    sshd
    sshd Service
    Service JSON Snippet (from netstat and /proc)
    JSON Snippet (from netstat and /proc)
    

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    "ssh": {
    "exe": "/usr/sbin/ssh",
    "cmdline": [ "ssh", "servidor" ],
    "uid": "alanr",
    "gid": "alanr",
    "cwd": "/home/alanr/monitor/src",
    "clientaddrs": {
    "10.10.10.5:22": {
    "proto": "tcp",
    "addr": "10.10.10.5",
    "port": 22 },
    ssh
    ssh Client
    Client JSON Snippet(from netstat and /proc)
    JSON Snippet(from netstat and /proc)
    

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    First Dimension
    First Dimension:
    :
    Problems Addressed
    Problems Addressed
    ●
    Discovering and maintaining documentation (CMDB) using
    continuous discovery
    – Services, Systems, Dependencies, Switches, Interconnects, Configuration
    ●
    Monitoring and alerting: services, systems and compliance
    ●
    Managing compliance
    ●
    Mitigating risk
    

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    Why Discovery? (DevOps)
    Why Discovery? (DevOps)
    ●
    Documentation: incomplete, incorrect
    ●
    Dependencies: unknown
    ●
    Planning: Needs accurate data
    ●
    Best Practices: Verification needs data
    ●
    ITIL CMDB (Configuration Management
    Data Base)
    Our Discovery: continuous, low-profile
    

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    Second Dimension:
    Second Dimension:
    Unique Powerful Features
    Unique Powerful Features
    1. Continuous Discovery
    2. Discovery: Zero network footprint
    3. Centralized graph database
    4. We know everything that changes
    5. Discover and update dependency information
    6. Discovery and monitoring tightly integrated –
    discovery drives automation
    

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    (even more) Features...
    (even more) Features...
    7. Discovery and monitoring easily extensible
    8. Naturally scalable to > 100K systems
    9. Minimal network load
    10.Server failures distinguishable from switch failures
    11.Best practice and vulnerability alerts
    12.Multi-tenant support
    

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    Third Dimension:
    Third Dimension:
    Fully distributed work
    Fully distributed work
    Two philosophical underpinnings
    1. Monitoring and Discovery are fully distributed
    2. Reliable “no news is good news”
    Only responses to changes are centralized
    

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    Service Monitoring based on HA Technologies
    Service Monitoring based on HA Technologies
    ●
    Well-proven architecture:
    – reliable “no news is good news”
    ●
    Implements Open Cluster Framework
    standard (LSB and others – Nagios coming!)
    ●
    Each system monitors own services
    ●
    Can also start, stop, migrate services
    

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    How does discovery work?
    How does discovery work?
    Nanoprobe scripts perform discovery
    ●
    Each discovers one kind of information
    ●
    Can take arguments from environment
    ●
    Output JSON
    CMA stores Discovery Information
    ●
    JSON stored in Neo4j database
    ●
    CMA discovery plugins => graph nodes and relationships
    

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    A Few Canned Queries
    A Few Canned Queries
    allipports get all port/ip/service/hosts
    allswitchports get switch connections
    crashed get crashed servers
    shutdown get gracefully shutdown servers
    downservices get nonworking services
    findip get system owning IP
    findmac get system owning MAC
    unknownips get unknown IP addresses
    unmonitored get unmonitored services
    

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    OS discovery JSON Snippet
    OS discovery JSON Snippet
    { "nodename": "alanr-1225B",
    "operating-system": "GNU/Linux",
    "machine": "x86_64",
    "processor": "x86_64",
    "hardware-platform": "x86_64",
    "kernel-name": "Linux",
    "kernel-release": "3.8.0-31-generic",
    "kernel-version": "#46-Ubuntu SMP ...",
    "Distributor ID": "Ubuntu",
    "Description": "Ubuntu 13.04",
    "Release": "13.04",
    "Codename": "raring" }
    

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