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Igor Volovich, CISSP,CISM,CISA,CRISC,CIPP Scott Sattler, CISSP,CISM,CISA,CRISC,CCNP,CFE SIEM: GOLDEN GOOSE OR MONEY PIT ? © 2013 VOLOVICH | SATTLER

SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

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Page 1: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Igor Volovich, CISSP,CISM,CISA,CRISC,CIPP Scott Sattler, CISSP,CISM,CISA,CRISC,CCNP,CFE

SIEM: GOLDEN GOOSE OR MONEY PIT ?

© 2013 VOLOVICH | SATTLER

Page 2: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

In this session • SIEM 101 – a quick primer • Capabilities vs. technologies • Common SIEM pain points • Key concepts for a happy SIEM • Getting SIEM wrong • Doing SIEM right • Sensory instrumentation approach • SIEM service deployment models • Q & A Session

Page 3: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

SIEM functional components

SENSORY DATA SOURCES

EVENT/DATA AGGREGATION

EVENT/DATA CORRELATION

ALERTING REPORTING VISUALIZATION RETENTION ANALYTICS

EVENT/DATA COLLECTION

ENDPOINT SYSTEMS NETWORK SYSTEMS SECURITY SYSTEMS

APP INFRASTRUCTURE IDENTITY/ACCESS VULNERABILITY MGMT

CMDB/CONFIG

DATA INVENTORY

THREAT & VULNERABILITY MANAGEMENT PROCESSES (TVM/CSIRT)

Preparation

Detection

Analysis

Containment

Eradication Recovery

Debrief

Page 4: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to

examine events” Quality of detection, analysis unknown Complex platforms are difficult to manage ROI hard to measure, harder to achieve/show Vendors oversell and under-deliver Most issues are NOT technology failures Customer expectations unrealistic Vendor attitude is “fire-and-forget”

Page 5: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Key concepts

Objectives are primary, tradecraft secondary Focus on capabilities, not tools SIEM as a service in your enterprise First, know your (platform’s) purpose Second, have a plan Look for “Indicators of Compromise” Anything can be a sensor!

Page 6: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

How to get it wrong Forget strategy – let’s just collect stuff Not sure what to log – let’s collect it ALL! Look, a magic box - stuff goes in, qualified

actionable security alerts come out! It’s too noisy, this platform sucks It’s too quiet, we must be impregnable It’s too difficult, let’s just outsource I thought we were getting “security-

compliance-reporting-alerting” in a box?

Page 7: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Getting it wrong, part II Setup is a “Five-day Quick Start” – that’s it Finally, our long-awaited log dump is here! Wait, we need “people” to run this thing?! Hey, what’s this button here do? I know we didn’t buy it for this, but… Vendor also said it could bake tasty pies It’s ok, we’ll just hire a consultant to tune it Why can’t I have your log data? Executive buy-in – what’s that?

Page 8: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

How to get it right Focus on capabilities, not flashy gimmicks Assess your monitoring objectives Know your environment and data mapping Capture and mature existing processes Set clear goals for platform implementation Invest time into vendor/platform selection Recognize commitment of self-run SIEM Is self-hosted/self-managed for us? Talk to your CFO: CAPEX or OPEX?

Page 9: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Sensory instrumentation Develop use cases to inform your source

selection process Determine event/data acquisition constraints Parsing ability Event rates Payload fidelity

Don’t collect just because you can Focus on protection target, not infrastructure Smart sensors = intelligent events

Page 10: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Anything can be a sensor

email AV

routers/network infrastructure email

logs

user accounts

firewall

NIDS HIDS

event correlation

aggregation

event/data collection

proxy AV

web proxy

data repositories

Page 11: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Intelligent sensors - rich data Data Loss Prevention (Vontu) Next-Gen Firewalls (PaloAlto) Endpoint security agents (Bit9, CyberArk) NAC technologies (ForeScout) Purpose-built platforms (FireEye, Damballa) Web App Firewalls (Imperva) VPN/RAS systems (Juniper, Citrix) Wireline solutions, IPS (Netwitness, Solera) Identity Management systems

Page 12: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Deployment Options

Page 13: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Self-hosted, self-managed

Cl Ag Cr Vz Al An Rp Re

In-house █ █ █ █ █ █ █ █

MSSP

Page 14: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Self-hosted, MSSP-managed

Cl Ag Cr Vz Al An Rp Re

In-house █

MSSP █ █ █ █ █ █ █

Page 15: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Self-hosted, jointly managed

Cl Ag Cr Vz Al An Rp Re

In-house █ █ █ █ █ █ █ █

MSSP █ █ █ █ █ █ █

Page 16: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Cloud, MSSP-managed

Cl Ag Cr Vz Al An Rp Re

In-house

MSSP █ █ █ █ █ █ █ █

Page 17: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Cloud, jointly managed

Cl Ag Cr Vz Al An Rp Re

In-house █ █ █ █ █

MSSP █ █ █ █ █ █ █ █

Page 18: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Cloud, self-managed

Cl Ag Cr Vz Al An Rp Re

In-house █ █ █ █ █ █

MSSP █ █

Page 19: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Hybrid model, jointly managed

Cl Ag Cr Vz Al An Rp Re

In-house █ █ █ █ █ █ █ █

MSSP █ █ █ █ █ █ █ █

Page 20: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

Q A &

[email protected] [email protected]

No SIEMs were hurt in the making of this presentation.

Page 21: SIEM: Golden goose or money pit...SIEM pain points Too much data – not enough intelligence Very quantitative – “need more people to examine events” Quality of detection, analysis

© 2013 VOLOVICH | SATTLER | securelabs.net