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Our CTO, Ryan Hohimer has been working with big-data and data science since research work post-9/11, Person of Interest. Originally conceptualized idea in consultation with Counter Intelligence and Insider Threat SME
2009: PNNL funded initial research based on interest of stakeholders from IC community
2013: In parallel, Grad research @ Pepperdine University B-School. Searching for commercialization opportunities, found Ryan and technology. Identified more than 20 markets for technology, decided to pursue cyber first. Company founded, patents applied for and began tech transfer.
2015: Recognized for successful technology transfer to industry (FLC Award) and at the “Oscars of Innovation”, the R&D100 Awards.
2016: Released to commercial market and deployed at a client site with 5K employees --------------- Applicable to cybersecurity, fraud analysis, and the global movement of money, etc. Anywhere a human analysts is important. WHY they make decisions… HOW to make decisions… WHAT is important…
Training a CI or security analysis takes longer than even cyber. Because of scarcity, need solution that captures knowledge.
Most important in non-cyber: Hunt for information (knowing social media, etc.)
Approximately 4 percent of all malware alerts are investigated. - On average, organizations receive almost 17,000 malware alerts in a typical week but only 19 percent of these alerts are deemed to be reliable. Of the 3,218 reliable alerts, only 705 are investigated. This suggests that participating organizations do not have the resources or in-house expertise to detect or block serious malware.
Annual cost of the time wasted on malware containment Calculus Extrapolated hours per week 395; Extrapolated hours per year 20,533 Fully loaded wage rate*$62.00 Extrapolated cost per year$1,273,061 -------------------- Here’s a REAL WORLD Example – client People: Operates with 30% fewer Staff Process: Lowered Risk by examining more and previously ignored alerts Technology: Increased Internal Rate of Return (IRR) for existing multi-million dollar investments in security appliances and systems.
On left-- In a typical SOC without DarkLight, an analyst must consider each incoming alert. Due to the volume of these alerts, not all can be addressed. As you saw in the previous slide, it’s not uncommon for only 4% or so of the alerts that hit a given organization to even be analyzed.
On right – With DarkLight, each and every alert is analyzed – 24x7x365, eliminating the false positives. For those deemed to be true positives, DarkLight enhances the alert with actionable intelligence and notifies the analyst.
DarkLight is force multiplier where fewer analysts are needed for mundane tasks or to weed out false positives. More importantly, those analysts can be reassigned to more urgent activities like incident response, or hunting for the “one-percenters” – those threats that will do your organization the most harm.
Cyberseurity L1: Data Collection: Sensors protecting perimeter, making sense of raw data. Can give you evidence that something has occurred.
L2: Forensics/Threat Intelligence in structured form to defend, respond to attacks.
L3: Explosion of tools: Breach Analytics, Entity & User-based Analytics and Automation to try to make sense of the Data/Information are now measuring. Most take a data-centric (Machine-Learning) approach. Great for what the algorithm/data knows, but requires human “in the loop” for advanced reasoning.
L4: Humans analyzing data are most effective--but can’t scale. DarkLight leverages Knowledge Representation & Reasoning to put human “ON THE LOOP” (vs. IN the loop). It taps into their knowledge and experience but without the scaling limits.
Darklight is methodology taps into the KNOWLEDGE & EXPERIENCE of the analyst to intelligently interpret sensors. DL is application of your intelligence.
Analytics and automation software platform powered by the logic, knowledge, and experience of cyber analysts. Applies Artificial Intelligence to generate human-quality results at scale.
First and foremost – DarkLight is a force Multiplier, enhancing human reasoning. How? Because it’s the human analyst who has encoded the logic through a visual, easily understandable manner. While DL can infer conclusions about the data it analyzes, one of the most powerful capabilities is a reduction of false positives.
Second – DL helps to capture the analyst’s knowledge and reasoning process. We call this a PRO (a programmable reasoning object) and it’s what even a junior analysts or one new to the org can look at DL to see how the system uses knowledge to reason. Most importantly, when the inevitable “brain drain” does occur - the analyst walks out the door but the knowledge he’s developed stays behind.
Finally – I noted in the first slide that the tech behind DL’s is applicable to multiple domains. In part, this is because it’s sensor-agnostic and collects the output of any other sensor to build a graph from this output. DarkLight fuses this information from all different sources (including network, threat intel and knowledge specific to the enterprise and correlates separate sensor events. Normally this is a tedious and time-intensive activity done by the organization’s cyber-analysts. As a result, both the human and product investments the organization has made, work more effectively.
Here’s how it works:
DarkLight INGESTS and NORMALIZES real-time streams of network sensors + threat intelligence + sources of enterprise knowledge like directories, HRIS and any other, even legacy sources.
Data can come from your existing network security, monitoring and threat feeds (internal/external, commercial & public), intrusion protection and detection systems, either directly or through any collector like a SIEM. DL can ingest data in CSV, JSON or by directly querying sources such as Splunk. Remember: sensor agnostic
Next, DL Automates analysis of the ingested sensor and the other enterprise data sources -- Independent and separate of time, sequence or volume. Remember the PROs I mentioned earlier? Think of these like little data scientists, examining data and inferring conclusions about the results.
Once DL weeds out the false positives from the actionable threats, it can:
Feed an IR system for further investigation. With a significantly lower number of False positives and detailed, attributed and correlated information about the alert, the Analysts can now investigate more effectively. Can also feed a HelpDesk/Ticketing system for an automated or manual response according to the orgs policies Notify an analyst for direct investigation OR trigger Orchestration or or action by another product (eg. Programmatically informs the firewall to blacklist a malicious IP).
Identifies and Documents activities, patterns and anomalies, in the context of your enterprise, as defined by your analyst
About: 100K alerts per day from security appliances; Using Splunk to collect 2.5B. Tried, didn’t like Splunk application for enterprise security.
Timeline April 2015: Began co-development with PNNL; “Analyst’s Assistant” Jan 2016: Identified need to expand analytics between collector and analysts May 2016: DarkLight analyzes enterprise alerts ahead of IR system; Deployed to Production
Results: Example: 4636 to one (FEYE IPS alerts, matched to Nessus scan, elim false positives) FE IPS: Expanding to other programs, based on initial success
6 Senior analysts (3 senior) down to 4 analysts Deputy CISO: “Increased effectiveness from 5-15% to 90-95%” “Effectiveness”= # total alerts promoted to IR / #total alerts. Major improvement in time previously wasted by staff chasing FP reduction.
Once data is ingested into DarkLight, the Programmable Reasoning Objects (PROs) go to work – in real time – analyzing thousands or tens of thousands of events in seconds.
The purpose of these are to make inferences on sets of data whether contextual, working, or both. To put their use into perspective, each PRO acts something like an analyst assigned to finding correlations between different data sets and records ranging from thousands if not tens of thousands of logs.
In order to find any disruptive or dangerous activity analysts must spend hours or days searching for these patterns. DarkLight alleviates this by incorporating PRO reasoners to do this daunting task for the analysts, leaving them with a condensed data set to work with.
Several views work together to provide the full picture about a single event. The Working Memory view contains lists of PRO Output Types and indicates how many items are in each type. Clicking on a working memory type loads those events into the Events view where they can be sorted by date. Clicking on a single event populates the tabular Results view, the graphical Results Graph view, and the Processors View.
The Results Graph view is a node/link graph that describes the selected item in the Events view. It contains all of the properties and objects that have been attached to the event as it works its way through ingestors and PROs. Each new object gets a different color.
The incoming event is always blue in the graph. The other objects receive colors dynamically as they are drawn so objects of the same type (e.g., Employees) are not always the same color each time a new graph is drawn. Nodes that are connected to more than one object are colored black. This helps them stand out as they are typically of interest. Lines between nodes in the graph have a label on them indicating what kind of a link they are (data property, object property, or type).
PROs do the heavy lifting to analyze events, and can also: Notify by email or Publish reports or Orchestrate other security devices to take action (eg. Add malicious IP address to firewall black list or call any script
Done through scripts or JSON (not shown)
1. Trigger (Subscribed data-object activates the PRO) 2. Collect (PRO gathers up all the facts the PRO author has pointed it to) 3. Reason (PRO invokes the Description Logic reasoner) 4. Publish (PRO publishes what the PRO author specifies)
Cyber Liability - Insurance Risk Management and Preparation
Cyber Liability Insurance:
A proactive approach to
A NEW SAAS MODEL TO ADDRESS CYBER INSURANCE RISK
MANAGEMENT FROM ADAPTIVE SOLUTIONS
Our Presentation on Cyber Risk
• The Adaptive Solutions SaaS model in strategic alliance withWillisTowersWatson
• The Cyber Risk Insurance Market - background and current state
• Cyber Attacks… some current statistics
• Preparation of the “To Be” State: The Adaptive Cyber Security SaaS Platform
• DarkLight – enhanced cyber security effectiveness through ontology driven machine learning
SaaS based cyber liability risk management
• Adaptive Solutions LLC has announced a new cyber risk management program for enterprise
• Working withWillisTowersWatson, the largest broker of cyber insurance in the US, we will
develop programs for both insured and insurer
• Our solution will let you visualize data governance, lineage, traceability, retention, and
management throughout your organization with the Adaptive Metadata Management™ suite
• We will improve the effectiveness of cyber security efforts through analysis, deconstruction
and prediction of cyber attacks with our strategic partner DarkLight™
• These tools will better address the challenges of your operating environment with targeted
savings in insurance premium throughWillis
• We will provide post-attack analysis for leading insurance carriers and proactively prepare
digital assets to better withstand and recover from cyber attacks and further reduce expense
Key elements of the Adaptive SaaS offering –
what is involved ?
• Adaptive Metadata Manager, highest risk business unit first
• DarkLight Cybersecurity
• Implementation by Adaptive Solutions and Meta Informatics
• In partnership with WillisTowers Watson to design and deliver actual insurance products with
demonstrable effectiveness for Insurance carriers AND clients
The Cyber Risk Insurance Market - background
and current state
A quick background on the pervasive nature of Cyber Risk
• Cyber attacks are a constant threat to businesses around the world with vast sums of money
being spent to protect against them.
• While in 2015, 40 percent of attacks stemmed from ‘outsiders’, a surprising 60 percent were
actually perpetrated by company insiders.
• IBM, who produced the figures based on information from over 8,000 of their clients devices,
revealed that although 15.5 percent of such ‘attacks’ were caused inadvertently, 44.5 percent
were deemed to have been malicious.
• An insider is defined as anyone who has physical or remote access to a company’s assets. IBM
noted that although this would often be an employee, it can also mean business partners or
maintenance contractors – people you trust enough to grant system access to.
• Insiders not only have this access, they may also be aware of your weaknesses and thus exploit
them more effectively than an outside agent might be able to.
Cyber Risk Coverage – Market Players
• U.S. insurers are cautiously underwriting cyber coverage
• The biggest challenge is to understand the true nature of the underlying risk
• While there are about 50 insurers that are writing some cyber coverage, the
market is dominated by five underwriters:
• Ace Ltd.
• American International Group Inc.
• Beazley P.L.C.
• Chubb Corp.
• Zurich Insurance Group Ltd.
Cyber Risk Insurance Market – Background
• Cyber coverage represents a significant area of opportunity for underwriters
• Some analysts predicting that the size of the cyber insurance market will
grow to $10 billion in the next five to 10 years
• Although this market is immature at the moment, there is still value to be
found if insurers properly underwrite risk
• Currently, cyber coverage predominantly is written on a claims-made basis
and primarily covers third-party liability in the United States
• About 90% of the premium volume for cyber — estimated by Lloyd's of
London to be $2.5 billion in 2014 — covers U.S. risks
• The market will increasingly demand tools to mitigate risk and manage
claims adjustment expenses post attack
The Cyber Risk Insurance Market
• Cyber insurance has emerged as a response to growing number of data
breaches worldwide and the extent of damage that they cause to businesses. Data
breaches are perceived as one of the leading risks to businesses as, among other
factors, they can have a huge influence on the company earnings.
• In the United States, the average cost of cyber crime amounted to 12.69 million
U.S. dollars in 2014. As well as financial costs, cyber crime has a negative
impact on employee morale, business reputation and relations with the clients. It
is not surprising, then, that companies have started to look for ways of protecting
themselves against cyber threats.
Cyber Insurance Market – current state
• In 2014, 54 percent of global companies were insured against loss of income due to
data breach, while more than half of the companies without cyber liability
insurance considered purchasing it.
• The share of businesses with cyber insurance worldwide increased with company
revenue. Only 3.8 percent of companies with revenues lower than 2.5 million U.S.
dollars owned cyber insurance.
• Among companies with revenues exceeding five billion U.S. dollars, this number
was equal to 25.9 percent.
Cyber Insurance Market - current state
• In the United States, 33 percent of companies owned cyber liability insurance in 2014.
In that year, the U.S. industry sector with highest share of companies purchasing the
insurance was the financial services sector.
• The average limit of purchased cyber liability insurance by the U.S. financial
institutions sector amounted to 23.5 million U.S. dollars. More than 82 percent of U.S.
companies reported that they were able to buy cyber insurance that met their
needs in 2014.
• The companies not protected by cyber insurance cited a lack of insurance fitting their
needs on the market, as well as low policy limits or too high costs, as the reason for
their lack of protection.
Cyber Insurance - Challenges
• S&P said that cyber risk presents a “unique challenge” for underwriters
because neither frequency nor severity is predictable.
• Reliable Actuarial data is also unavailable.
• Metrics for cyber risk also are in the early stages of development, and
probabilistic models pose high levels of uncertainty, mostly because of the
unpredictable human behaviors associated with cyber attacks” the report said.
• Other challenges for underwriters include limited and insufficient disclosures
about cyber attacks, the report said.
crime in the
• According to the IC3, the monetary damage caused by reported cyber
crime in 2014 amounted to more than 800 million U.S. dollars.
• That year, the U.S. state with the highest amount of losses was California
with over 131 million U.S. dollars in reported cyber crime damages.
• The average cost of a company-directed cyber crime attack in the United
States was 15.42 million U.S. dollars.
• Based on the type of attack, industry figures estimate the number of
days necessary to solve a cyber attack on a company can take up to 62.7
• The most common types of cyber attacks experienced by U.S. companies
as ofAugust 2015 were viruses and malware.
• According to a 2015 survey of U.S. companies, the most popular cyber
securities deployed were advanced perimeter controls, firewall
technologies, and extensive usage of encryption technologies.
crime in the
• Despite these efforts to protect the company from outside
cyber attacks, there are many employee activities that render a
company vulnerable, such as mobile device usage or remote
• Other obstacles to implementing more robust cyber security
solutions for businesses are the lack of funds as well as the lack
of clarity regarding best practice.
• Overall, 42 percent of SMB owners in the United States
regarded cyber security expenditure as a cost of business with
36 percent of IT security layer spending being directed towards
the network layer.
• Furthermore, 27 percent of internal costs due to cyber crime
were allocated towards detection.
Total Cost of Cyber Crime
The statistic shows the amount of damages caused
by cyber crime reported to the IC3 from 2001 to
2015. In the last reported period, the annual loss of
complaints referred to the IC3 amounted to 1.07
billion U.S. dollars, up from 781.84 million U.S.
dollars in 2013.
In 2014, the United States accounted for 83.96
percent of complainant losses.
No data available on reported cyber crime losses in
The numbers refer to internet crimes reported to
the governmental Internet Crime Complaint
Methodology of evaluating loss amounts: FBI IC3
Unit staff reviewed for validity all complaints that
reported a loss of more than $100,000. Analysts
also converted losses reported in foreign
currencies to dollars. The final amounts of all
reported losses above $100,000 for which the
complaint information did not support the loss
amount were excluded from the statistics.
Type of Cybercrime and Loss
This statistic presents the types of
cyber crime with the highest amount
of victim losses in 2015. During the
reported period, online confidence
fraud accounted for 203.39 million
U.S. dollars in reported victim losses.
In 2014, the United States accounted
for 83.96 percent of complainant
Types of Cyber
This statistic shows the types
of cyber crime attacks most
commonly experienced by
companies in the United
During a 2015 survey of 58 U.S.
companies, it was found that
97 percent of respondents had
experienced malware attacks.
The most common type of
attacks were viruses, worms
Average cost of a breach
The statistic shows the average
organizational cost to business in the
United States after a data breach. In
2016, the average cost to businesses
affected by a data breach in the
United States amounted to 7.01
million U.S. dollars.
Total breach costs include: lost
business resulting from diminished
trust or confidence of customers;
costs related to detection,
escalation, and notification of the
breach; and ex-post response
activities, such as credit report
Average annual costs
related to Cyber Attacks- by
This statistic shows the average
annualized costs caused by cyber
crimes in the United States as August
2015, sorted by affected industry
That year, cyber crime caused an
average annualized loss of 16.45
million U.S. dollars in the technology
Cybercrime Loss Given a
This statistic shows the estimated
damage a successful cyber attack will
cost a U.S. business.
In 2015, the maximum total annualized
cost of cyber crime committed against
U.S. companies amounted to 65.05
million U.S. dollars.
Number of days to
resolve a Cyber Attack
This statistic shows the average
number of days necessary to
resolve a cyber attack in U.S.
companies as of August 2015,
sorted by type of attack.
That year, U.S. companies need an
average of 41.3 days to resolve
IT Environments and
This statistic gives
information on the IT
environments targeted by
cyber attacks worldwide in
2015, sorted by industry.
During the survey period,
it was found that 34
percent of cyber attacks
aimed at the professional
service industry were
targeted at corporate or
What do these
statistics tell us
Cyber Threats ?
• Threats posed by internal actors is the most significant;
in Finance and Insurance, this is effectively 100% of the
source of cyber risk
• E-Commerce is the largest threat to retail and travel;
Point of Sale fraud is largest for Food & Beverage
• Data breaches increasing in size and number of affected
• Time to Resolution has improved, highlighting industry
education and prevention
• Most damaging attacks remain internal
• Preparation is the best policy
• So how to prepare ?
Preparation of the “To Be” State: The
Adaptive Cyber Security SaaS Platform
How does Adaptive Solutions propose to
revolutionize Cyber Liability Underwriting?
• Rudimentary underwriting
• Lack of defined risk metrics
• No means of identifying affected
• No traceability or lineage for post-
breach analysis and remediation
“As Is” State
• Identify and measure against key
metrics impacting risk
• Use preventative and analytical
tools to understand depth of event
• Create a “data inventory” which
catalogues both data and lineage
approach • Inventory of key data assets and
traceability/lineage for breach
• More effective cyber security
• “Learning” bots to assist with
volume of attacks
“To Be” State
Cyber Liability Insurance – Underwriting
• What EXACTLY is being protected ? Or what exactly was affected
by the breach ?
• What are the key underwriting metrics ?
• How is the risk priced ? How is this determined ?
• What are typical policy exclusions ? Retention ?
• What are typical loss scenarios ? Recovery scenarios ?
This all needs to be discussed, documented, and linked to
technology that offers actionable solutions
The End State must specifically address the Threat Matrix
• External actor
• Access through a vendor
• Through ISP
• Through DNS/Brute Force
• Internal actor
• Disgruntled employee
• Actively placed sleeper mole
• Internal incompetence (like passwords in a desktop Folder
labeled “Passwords” – Come on man !)
• Things in Common
• Major Losses
• Lasting Damage
“To Be” State - Cyber Risk Management with the Adaptive Repository Orchestration
How does it work ?
• We combine the disciplines of robust data governance and cyber
security through the application of world class technology
• Catalogue the key data assets by business unit and function
• Inventory the data assets, establish lineage and relation
• Implement an integrated cyber security solution
• Our solution lets you understand the key risk metrics BEFORE the risk
• How stable and “orderly” is the client data environment ? Data quality ? Points of
access ?You better know before you bind the risk…
• How do we do that ? Adaptive for lineage, governance, security, permissioning,
versioning, and data tracing; DarkLight for cybersecurity enhancement
• Understand the data environment
• By Business Unit
• By Data Source
• Understand the network environment
• Number of IP and Email addresses
• Web Sites
• IoT access
• Create enterprise data lineage and traceablity to establish base case
and identify data quality, loss, and retention issues
• Catalogue the data assets being protected and identify the key
stakeholders of each
• Integrate the DarkLight cyber solution with the Adaptive Metadata
Platform to enhance cybersecurity
• Integrate the underwriting review to prepare the SaaS solution for
the specific client
Cloud ServicesBig Data Platform
- DG Maturity
- DM Compliance
- DQ Maturity
- DA Maturity
- DG Ownership
* Build customized UI by enhancing
Adaptive’ s UI
Client’s Data Landscape
- Data Stewards
- Data Owners
- Scheduled Reports
- Monthly DQ/DG Snapshots
- Monthly Data Compliance
- Alerts & Notifications
- On Demand Reports
- Data Modeling
- Data Governance
- Data Quality
- Production Support
Key elements of the Cyber Risk Management with the SaaS model
Client Service Provider Onsite Service Provider Offsite
Data Owners Business Analyst
Legacy systems and cyber risk management
• Legacy systems pose a unique risk to an organization
• Failure to migrate to modern platforms complicate risk
management and recovery post attack
• Most firms delay migration due to theThree P’s:
• pain, personnel, and price…
• We greatly reduce theThree P’s in legacy migration and
management with automated data discovery and documentation
• We offer this on a SaaS basis using open standards
Data governance and lineage tracing –
A live client example
Bank Client – establishing data governance and lineage with
huge amounts of data
• The technical truth of architecture and data flow within a large organization
is nearly impossible to understand for any user without technical
• We automatically scan the dataflow to identify all of these objects and links.
Because programs, procedures and scripts do refer to exactly these
technical objects, this may cause a huge complexity in the meta data
• As for example, one Client’s Group Business Intelligence Repository holds over
320,000 tables, columns, views, entities, attributes, report fields and dimensions.
• Approximately 10% are interesting from business view.The others are used for layer
concept, arch. Principles, compliance, performance, Interfaces etc.
When scanning the raw technical data automatically to generate data models and data
lineage, one very important aspect are considerations on how to manage the complexity for
different users, and how that data is presented.
In this example, assume for a moment you’ve been hacked… and you need to identify
all the affected data - In this Client case, search for affected data elements with a name
“customer” returns 45,315 Results (across all object types)
be daunting and
difficulty to track
down all of the
Filter to Relational.Column reduces to 8,243
Classification “Data Store” equal Group Data Pool reduces to 524
Presentation Layer /
Search Results IV
Classification “Table Layer” equal Business Data reduces to 123… now its manageable
Presentation Layer / Classifications
How to manually classify, track and trace lineage for > 300,00 Objects? You can’t…
The “Rules Engine” can inherit classifications via CWM connection
Data Store: LDD
Data Store: LDD
table layer: LDD Views
Data Store: LDD
Table: layer: LDD Views
Data Store: LDD
Table layer: LDD Views
layers for lineage
Although restrictions for data
flow are defined, lineage can
become very big and complex.
The goal is to be able to
automate the lineage tracing
process and narrow presentation
to mazimize effectiveness
Zoom of Lineage
And tracing lineage can become overwhelming – the majority of expense post
cyber attack is identifying and tracing affected data…
Managing the Presentation Layer /
Virtual business layers for lineage
Adaptive uses smart algorithms to traverse through the lineage and pick only defined
columns to show them in a textual view: “Column is derived from CEE IF Fields”
Rules can be based on
- Owning Schema
Rules allow the user
define and manage their
data environment from
top to bottom
Investigation one – identify the affected data
1. Locate the desired data
element (in this case, a
2. Display the reverse lineage
3. Identify the Source Field
4. Investigate single
transformation steps further, if
This allows the client to manage
the complexity and volume of the
Investigation two /
establish and understand the lineage
Source Code: Insert Statement at given line number.
Lineage SVG Graphic
Selfmade “PLSQL” object view of column
PLSQL object view. Gives line number.
Repeat for other 3 Targeted Fields
interconnection of the
target data element
This graphic shows the lineage of
just one of these data source
columns in Reporting.
Failing to understand internal data
lineage is not a good idea. Our SaaS
soultion will link data to business
terms and concepts to trace data.
Post Cyber Attack, this is the major
driver of expense in post attack
investigation, management and
As a result of this work, our Client
gained a deeper understanding and
tangible simplification of their data
In this example, the Client used
Adaptive to link business terms and
concepts directly to source data to
establish lineage and a governance
framekwork for regulatory compliance
and financial reporting.
This To Be State allowed them to
directly link business concepts and
source data, using automated lineage
tracing and data governance
Report Field Source Table Description
B2 - COLL
S3_EXPOSURE Basel 2 figure. Collateral value
from SAS BEFORE Haircuts
(deductions) divided to
after recoverability of collateral;
Collateral value that considers
all collaterals independent of B2
eligibilitiy. Collateral Market
Value from SAS; distributed by
SAS Coll Type; capped with
B2 - COLL:
S3_EXPOSURE Basel 2 figure; Collateral value
from SAS AFTER Haircuts
divided to Exposure-sets;
Optimization after recoverability
The distributed collateral
acceptable value in the way of
SAS CRR acceptable value
algorithm in EUR.
AI-Driven Analytics and Automation
2009: PNNL funded research
2013: Company founded to transfer technology to market
2016: Commercial release, deployed in production
► Artificial Intelligence based on Semantic Graph Analytics
Patented Advanced Reasoning Platform; Two granted, several in process
Proprietary AI engine captures, automates and scales human expertise
Applicable to cybersecurity, fraud analysis, and the global movement of money,
► Scarcity/high turnover of seasoned cyber analysts
Outnumbered and overworked - projected shortfall of 1.5 million Analysts by 2019(1)
Enterprise knowledge leaves with analyst – 18 mo. ramp to get “proficient”
► Staggering volume of cyber attacks creating “big data” issue
Existing technology investments are underutilized while threats persist
Staff is “drowning in data”
Challenges in the Market
Challenges in the SOC
Not enough analysts to address the volume of alerts New or junior Analysts not productive enough
Not enough analysts to address the volume of events Can’t fill open requisitions for SecurityAnalysts
Knowledge leaves org when Analysts leave
Too much time spent monitoring rather than
Incident response time takes too long
No centralized process or tools OR
Too many tools, not enough coordination between
Analysts waste time manually attributing and
documenting incident response
Analysts waste time chasing down false positives No Analysts dedicated to hunting
Existing alerts or select security feeds are ignored Other – domain specific
Drowning in Data vs. DarkLight
Source: “The Cost of Malware Containment,” Ponemon Institute, January 2015 Survey of 630 IT / IT Security Practitioners in US responsible for detecting, evaluating and/ or containing malware infections.
received on average
by an organization in
a typical week
Alerts investigated; Only 705
Exposed to risk of remaining 15%
4%WASTED: 395 Hours/week due to False
LOSTVALUE: $25K/week or
Typical Industry Experience Customer, deployed in production
Alerts Examined n=9500
Lowered Risk by
Improved IRR and
With 30%fewer staff,
The Cybersecurity “Big Data” Problem
Science of Security &
Human Intelligence and Reasoning
DarkLight™ - Human-quality analytics, at scale
Fuses data from disparate intelligence sources
Unifies network sensors + threat intelligence + enterprise context
Improves IRR on existing security investments
Captures analyst knowledge for retention by the enterprise
Augments deductive and investigative skills
Prevents “brain-drain” while accelerating training of new staff
Force-multiplier which enhances human reasoning
Acts as aVirtual Analyst, improving performance by 10X to 100X+
Advanced, AI-based reasoning able to infer conclusions
DarkLight Reference Model
Threat Intelligence - Internal/External
Feeds Incident Response System
AD, Legacy Data Sources, HRIS,
TriggersOrchestration / Action
in other product
Improved ROI: Doing more with 30% fewer analysts by reducing false positive alerts
Improved Situational Awareness: Now analyzing 220 previously ignored data streams
Expanding to threat hunting
5000 employees, One of 17 DoE National Labs; Performs classified and unclassified research for
DoE, DoD, DHS and other government agencies.
100K+ alerts per day, 2.5B events/week through Splunk
“Increased our effectiveness from 5-15% to 90-95%”
BJ Stephan, Deputy CISO
DarkLight PROs (Programmable Reasoning Objects) analyzing data
Once data is ingested into DarkLight, the Programmable
Reasoning Objects (PROs) go to work – in real time –
analyzing thousands or tens of thousands of events in
The purpose of these are to make inferences on sets of
data whether contextual, working, or both. To put their
use into perspective, each PRO acts something like an
analyst assigned to finding correlations between different
data sets and records ranging from thousands if not tens
of thousands of logs.
In order to find any disruptive or dangerous activity
analysts must spend hours or days searching for these
patterns. DarkLight alleviates this by incorporating PRO
reasoners to do this daunting task for the analysts,
leaving them with a condensed data set to work with.
Results: Summary and Graph View
Several views work together to provide
the full picture about a single event. The
Working Memory view contains lists of
PRO Output Types and indicates how
many items are in each type. Clicking on a
working memory type loads those events
into the Events view where they can be
sorted by date. Clicking on a single event
populates the tabular Results view, the
graphical Results Graph view, and the
Since different users prefer to see
information in different ways, the user
may select which views are shown – and
save them as a perspective.
Graph View of Event with full attribution
In this example, DarkLight correlates a FireEye event with a vulnerable host, attributing CVE, device and employee details.
The ResultsGraph view is
a node/link graph that
describes the selected
item in the Events view.
It contains all of the
properties and objects
that have been attached
to the event as it works
its way through ingestors
Each new object gets a
DarkLight Event Orchestration
PROs do the heavy lifting to reason and analyze, saving time
…and based on the results, can alert, notify or orchestrate other systems to take action
Graph Databases – Big Data
• A graph is a data
• A graph holds data
• Schema (ontologies)
• Facts (assertions)
• Also known as an Inference Engine
• DARKLIGHT is a framework for
supporting multiple reasoners
• Each DARKLIGHT Reasoner (called a
PRO) examines the known facts
and asserts new facts based on the
axioms of cybersecurity.
Old Ineffective Method:
• Read all facts into a single
• Manage the logical consistency
of the large graph
• Use a single Reasoner over the
All facts asserted into a graph
MUST be logically consistent or
the Reasoner will not function.
- AND -
The larger the graph the harder
it is to keep it logically consistent.
Our Innovative Method:
• Read all facts into a single
• Manage the logical consistency
of small subgraphs as they need
to be reasoned over
• Use MULTIPLE Reasoners over
the graph, not just one
It is easier to maintain
consistency in many
smaller graphs than
one large graph.
Hierarchy of PROs
(Main Semantic Graph)
Known Facts Known & Inferred Facts
DARKLIGHT is a
The PRO Lifecycle
DarkLight PROs in Use
Contextual Knowledge Maintenance
*AttributedFEWebInfectionAlert Attributes SuspectPing
BlockedFEIPSAlertNotification 1102 – The audit log was cleared.
ContextTypeCountReport 4672 – Special privileges assigned to new logon.
DHCPRecordCleanup 4798 – A user’s local group membership was enumerated
FEIPSAlertForVulnerableHost 4799 - Security-enabled local group membership enumerated
FEIPSAlertForVulnerableHostNotification 5156 - Windows Filtering Platform has allowed a connection
FEIPSAlertReport 5140 - A network share object was accessed
FEIPSAlertReportNotification 7045 - A service was installed in the endpoint
FEIPSAlertWithHostVulnerabilities 4624 - An account was successfully logged on
FEIPSAlertWithVulnerability 4663 - Attempt was made to access an object, File or Registry
Force-Multiplying “Virtual Analysts”
• DarkLight is the only patented system that embraces the human
decision making process and knowledge to combat cyber threats.
DarkLight was created, tested, and proven at one of the nation's most
advanced research laboratories, spanning more than four years of
• DarkLight intelligently processes the massive data streams from a
current network and security appliances through a patented formal
Description Logic Reasoning Framework and Semantic Graph
• Unlike all other workflow-driven or machine learning-based
automation tools, this approach more effectively models normal
and abnormal user and network behavior.
• DarkLight’s Reasoning Engine is used to interpret and analyze facts
using an analyst’s unique knowledge of cybersecurity and the
enterprise, including the policies and compliance requirements of the
organization they are protecting. By utilizing the analyst rather than
black box or statistical models, the system becomes a true force
multiplier of expert experience and knowledge.
Applying DarkLight PROs to Detect Insider Threat
• By representing common sense knowledge from the cybersecurity
community and the knowledge from your enterprise's cybersecurity analysts,
tasks and data interpretation can be efficiently and intelligently automated.
• Because the DarkLight PRO (Programmable Reasoning Object) is created by
the security analyst themselves, it thinks and works like a human, and it can
be created to find any correlations and patterns between data sets.
• This gives your analyst the ability to create custom PRO's to track whatever
activity they deem necessary to keep your enterprise secure.
Ontologies and Threat detection
• Thought leaders at the CERT InsiderThreat Center at Carnegie Mellon's Software
Engineering Institute (SEI) have recently released new model concepts to help insider
threat programs to implement more effective controls.
• Based on cases from more than 1000 organizations, the research paper and models
have been several years in the making and provides a standardized method of
expression for indicators of potential malicious insider activity.
• They have identified an ontological approach to the problem and have provided the
industry with an InsiderThreat Indicator Ontology (ITIO).
• An ontological approach provides a standard common language with which to
represent and share knowledge, a factor they have identified as currently lacking
within the threat intelligence community.
Applying DarkLight PROs to Detect Insider
Examples of InsiderThreat PROs:
• Track group membership over time
• Detect off-hours system usage
• Detect uploading to known file-storage locations
• Detect unusual program execution
• Detect unusual printing activity
• Correlate when a member of a group decimated by layoffs uploads to a known location
• The InsiderThreat Indicator Ontology
Superior performance through reasoning
• DarkLight approaches the Cyber Security problem by allowing analysts to
explicitly establish what is the "normal" user behavior baseline in the context
of the enterprise business model and operations.
• For example, a compensation specialist working in HR should not be downloading customer
data; that is not part of the employee's normal user profile or approved behavior or UEBA.
• Understanding every employee, vendor and customer profile and behavior is
at the heart of what DarkLight gives the internal enterprise cyber security
analysts, followed by the ability to alert and act quickly.
• DarkLight offers the user a means to perpetuate their know-how via our
exclusive PROs. Other UEBA providers require an entity to use their machine-
learned models of the user and/or its peers.
Operationalizing the ITIO
• You can put CERT’s ITIO (as well as models like STIX,CybOX, OpenIOC and others) to work today with
DarkLight, for a force-multiplying, cyber analytic and automation platform.
• Import the InsiderThreat Indicator Ontology to DarkLight and the general concepts of the ontology are
mapped to real-time data of your organization.
• As an example, data of the “Actors” are mapped to “People & Organizations” of the company,
immediately leveraging the ITIO. Once this mapping has occurred, the hard problem of InsiderThreat--
identifying the subtle changes in an employee's behavior--can be identified much more easily.
• DarkLight helps you:
• Find the indicators
• Identify exfiltration
• Identify I.D. theft and fraud
• Collect the intelligence needed to allow efficient forensic investigations of affected assets.