Prezentace z konference Virtualization Forum 2019 Praha, 3.10.2019 Sál B

1. Miroslav Bajgar
Sales Engineer EEUR
Inteligentní ochrana osobních údajů (a citlivých dat)
v procesu digitální transformace a přechodu do cloudu.

2. 100x reduction in
malware incidents when
deploying Forcepoint.1
Forcepoint
Web & Email
Security
Forcepoint
Data Guard
Eliminates costly and time-consuming
manual data transfers of highly-
regulated, sensitive data.
Forcepoint
NGFW
Cuts network expenses by 50%, reduces
cyberattacks by up to 86%, slashes
incident response time as much as 73%.
Forcepoint
CASB
Broadest cloud application support with unique
customized risk assessment based on user
behavior and data access classification.
Forcepoint
Insider Threat
The most comprehensive
understanding of user intent,
trusted on over 1M endpoints.
Forcepoint
DLP
Gartner Magic Quadrant
Leader in Enterprise DLP
9 consecutive times.
Forcepoint
UEBA
The leader in actionable
insights based on
risk-adaptive scoring.
BEST IN CLASS CAPABILITIES

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We as Forcepoint are disrupting the security industry
• By delivering a unique human-centric cybersecurity approach
• With the goal of protecting users and data
• Frictionless and at scale for cloud, on-premises, and hybrid
Forcepoint: the Company

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Why are we disrupting the industry through
human-centric security?
Making the
Perimeter Intelligent

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How are we doing Human-Centric Security?
Making the
Perimeter Intelligent

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Lets take a step back. What is the idea of perimeter security?
You are building a wall around what you want to protect
Building a Wall Around What
You Want to Protect

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• The wall is not high enough
What are the options to build that wall?
Building a Wall Around What
You Want to Protect

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Outcome

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• The wall is too high
What are the options to build that wall?
Building a Wall Around What
You Want to Protect

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Outcome

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• The wall is just right!
What are the options to build that wall?
Building a Wall Around What
You Want to Protect

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Single point of failure: delay, scalability, etc.
Perimeter needs a template what to look for.
What happens before and after the perimeter: It is not analyzed!
Full trust behind the perimeter. Not everybody is an angel!
• Malicious insiders and accidental threat activity
Data is flowing like meandering rivers from on-premises to the cloud
• New paradigm
The wall is just right! So why is
the perimeter still not enough?

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Enrich the perimeter through two dimensions:
data and users
Goal is to make the perimeter smart!
Also understand what is happening before
and behind the perimeter
How? Behavioral Analytics at the user level—
not at the perimeter
What is the Solution Now?
A different approach to cybersecurity

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Does This Compromise Privacy?
No – you need to change the perception and view:

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Compliance (goal: trust of shareholders) is built on
control frameworks
• This is not about controlling the user
• This is about controlling the risk
This is your liability based on the
compliance frameworks
No – you Need to Change the
Perception and View:

16. Step 1
Analyzing Behavior before and after
the perimeter. Zero Trust
Step 2
Dynamically feeding back the risk into the
policy. No static policies!
Step 3
Making a decision based on context, not just
blanket black and white policies
Putting this all together
We already have the idea of zero trust.
This is the first incarnation in 2 steps:
This is about using behavior analysis to make the perimeter intelligent

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Use Case 1: Risk-Adaptive Dynamic Usage IT Director (Henry)
Monday
January 21 @ 10am
Risk Score: 30
Working while on PTO
+ Additional Context
Risk-Adaptive Protection
No enforcement action
Risk Score: 50
Risk-Adaptive Protection
Video capture activated
Evidence available for investigation later
Thursday
January 24 @ 1pm
Remote login with
change of UserID
+ Additional Context
Wednesday
January 30 @ 9pm
Risk Score: 80
Copy file to staging area
Risk-Adaptive Protection
Copy allowed but file encrypted
No corporate data at risk
+ Additional Context
Saturday
February 9 @ 6am
Risk Score: 95
Bulk copy to USB drive
Risk-Adaptive Protection
Action is blocked & account is locked
Avoided $10M breach and proof that
Henry is the attacker is available
+ Additional Context

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From
Blocking
To
Empowering
Graduated Enforcement Based on Risk
Removing friction to enable productivity without sacrificing security

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You have successfully installed and deployed Data Loss Prevention
You have 3,000 users. Based on statistics:
• You have 3,000 incidents per day
• You need to hire five Data Scientists to handle the top priority 250 incidents
• The rest is ‘flying below the radar’
• People learn the rules. More flying under the radar
• Three lower incidents could mean a significant risk
• You need people to go through PI data. GDPR
What if a machine learning algorithm bubbles up risk and feeds the
result back dynamically into a DLP policy?
Use Case 2: The Operational Dilemma

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Data Swamp
Limited visibility of data
Unknown security breaches
Policy framework in place
Visibility into all data sources
Audit-only to reduce friction
Post-breach analytics
Understand what has happened
Change policies retrospectively
Individualized, automated
enforcement based on risk to
stop or prevent
data loss
All other employees
unaffected within a frictionless
environment
Risk-adaptive and dynamic
feedback of risk into
the policy
EnterpriseMaturity
Level 0 Level 1 Level 2 Level 3
Proactive Automation
Understanding
Visibility
The Dynamic Data Protection Maturity Curve
From data swamp to automated security action

21. Protect your users and data
without impacting usability
Empower your business with frictionless
security that runs
in the background
Human-Centric Security for a Smart Perimeter
Lower risk of brand or financial penalties
resulting from data breaches
Move from passive data loss
prevention to active Dynamic
Data Protection
Protect Empower Lower Risk Active

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