GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
2. Temporal instant types
● DateTime
○ with timezone (logical zone or absolute offset from UTC)
● LocalDateTime
○ without timezone
● Date
○ without timezone
● Time
○ with absolute offset from UTC (logical zones needs date to map to offset)
● LocalTime
○ without timezone
3. Duration type
● Represents a number of
○ years
○ months
○ days
○ hours
○ minutes
○ seconds (and fractional seconds)
● years and months (and quarters) convertible
● (days and weeks convertible)
● hours, minutes, and seconds convertible
● No conversion between the three groups above!
4. Syntax
No new syntax is introduced.
All handling of temporal values is done through functions:
● datetime(...)
● localdatetime(...)
● date(...)
● time(...)
● localtime(...)
● {datetime,localdatetime,date,time,localtime}.truncate(...)
● {datetime,localdatetime,date,time,localtime}.{statement,transaction,realtime}(...)
● duration(...)
● duration.between(...), duration.inMonths(...), duration.inDays(...), duration.inSeconds(...)
5. Ways of creating temporal instants
● Parsing from ISO 8601 string
● Creating from components
● Getting the current instant
○ using different clocks: statement, transaction, realtime (one of them being default)
● Adding a Duration to an instant
● Converting from other instants
Ways of creating Duration
● Parsing from ISO 8601 string
● Creating from components
● Compute difference between instants
6. Parsing Temporal Instant from ISO 8601 string
● datetime($string), localdatetime($string), date($string), time($string), localtime($string)
● date: YYYY-MM-DD, or YYYY-Www-D, or YYYY-Qq-DD, or YYYY-DDD
(calendar date, week date, quarter date, ordinal date respectively)
○ dashes can be omitted, smaller units can be omitted
○ year with a sign prefix can be arbitrarily long, but then dashes may not be omitted
● time: HH:MM:SS.sss
○ colons can be omitted, smaller units can be omitted
● offset: ±HH:MM or Z
○ colons can be omitted, smaller units can be omitted
● zone: [name]
7. Parsing Temporal Instant from ISO 8601 string
● datetime($string)
○ <date>T<time><offset><zone>
○ either offset, or zone, or both
○ smaller units may be omitted
● localdatetime($string)
○ <date>T<time>
○ smaller units may be omitted
● date($string)
○ <date>
● time($string)
○ <time><offset>
● localtime($string)
○ <time>
8. Creating Temporal Instant from components
● datetime({...}), localdatetime({...}), date({...}), time({...}), localtime({...})
○ Date based:
■ year
■ quarter
■ month
■ week
■ weekYear
■ day
■ ordinalDay
■ weekDay
○ Time based:
■ hour
■ minute
■ second
■ millisecond
■ microsecond
■ nanosecond
○ Other:
■ timezone - the named timezone or offset if no name (supported by zoned types)
■ offset - as a formatted offset string (supported by zoned types)
■ offsetMinutes (supported by zoned types)
■ epochMillis (only supported by DateTime)
■ epochSeconds (only supported by DateTime)
■ epochNanosOfSecond (only supported by DateTime)
9. Getting the current Temporal Instant
● datetime(), localdatetime(), date(), time(), localtime()
The above uses the (implementation specific) default clock, which is one of the below:
Same point in time every invocation in the query:
● datetime.statement(), localtime.statement(), et.c...
● datetime.statement($timezone), et.c...
Same point in time every invocation in the transaction:
● datetime.transaction(), localtime.transaction(), et.c...
● datetime.transaction($timezone), et.c…
Actual time at invocation:
● datetime.realtime(), localtime.realtime(), et.c...
● datetime.realtime($timezone), et.c...
10. Converting Temporal Instants between types
● datetime({...}), localdatetime({...}), date({...}), time({...}), localtime({...})
● date - selects the date from a Date, or DateTime, or LocalDateTime
● time - selects the time from a Time, LocalTime, DateTime, LocalDateTime
● datetime - selects both the date and time from a DateTime or LocalDateTime
Examples:
● datetime({datetime:$aDateTimeOrLocalDateTime, timezone:"..."})
● datetime({date:$aDateOrDateTimeOrLocalDateTime,
time:$anotherDateOrDateTimeOrLocalDateTime})
● datetime({datetime:...}), datetime({date:..., time:...})
datetime({datetime:..., timezone:...}), datetime({date:..., time:..., timezone:...})
● localdatetime({datetime:...}), localdatetime({date:..., time:...})
● time({time:...}), time({time:..., timezone:...})
● date({date:...})
● localtime({time:...})
11. Parsing Duration from ISO 8601 string
● duration($string)
● unit based format:
PnYnQnMnWnDTnHnMnS
○ zero valued component can be omitted
○ T must precede time-based section (Hours and smaller)
○ Smallest component may be fractional
○ Examples: P15M, P1DT3H, PT48H, P1.5W
● date-and-time based format:
P<date>T<time>
○ smaller units may be omitted
12. Creating Duration from Components
● duration({...})
○ years
○ quarters
○ months
○ weeks
○ days
○ hours
○ minutes
○ seconds
○ milliseconds
○ microseconds
○ nanoseconds
13. Compute Duration as difference between Instants
Computing the duration from instant a to instant b:
● duration.between(a, b)
○ uses natural units
● duration.inSeconds(a, b)
○ uses hours, minutes, seconds
● duration.inDays(a, b)
○ uses days
● duration.inMonths(a, b)
○ uses months and years
14. Adding a Duration to a Temporal Instant
Example: date("1984-10-11") + duration("P33Y5M3D")
Note that this is not associative:
● (date("2011-01-31") + duration("P1M")) + duration("P12M")
○ date({year:2012, month:2, day:28})
● date("2011-01-31") + (duration("P1M") + duration("P12M"))
○ date({year:2012, month:2, day:29})
15. Truncating a Temporal Instant
datetime.truncate($unit, $instant)
localdatetime.truncate($unit, $instant)
date.truncate($unit, $instant)
time.truncate($unit, $instant)
localtime.truncate($unit, $instant)
Supported units:
● millennium
● century
● decade
● year
● weekYear - the first day of the first week of the week year of the specified instant (may be in december of previous year)
● quarter
● month
● week
● day
● hour
● minute
● second
● millisecond
● microsecond
16. Comparing and ORDER BY
● Temporal Instants comparable to other instants of same type
● ORDER BY uses comparison order for Temporal Instants
● ORDER BY groups by type when ordering by field with mixed types
● Duration values are NOT comparable
○ because different months are of different lengths, and not all days are equally long
● ORDER BY for Duration based on average length of days and months
17. Ordering durations - is the order consistent?
● PT672H = P28D ≤ P1M ≤ P31D = PT744H ; P1M ordered as: PT2629746S = PT730,485H
(shortest possible: February)
● PT1416H = P59D ≤ P2M ≤ P62D = PT1488H ; P2M ordered as: PT5259492S = PT1460,970H
(shortest possible two: Jan-Feb or Feb-Mar, longest possible two: Jul-Aug or Dec-Jan)
● PT2136H = P89D ≤ P3M ≤ P92D = PT2208H ; P3M ordered as: PT7889238S = PT2191,455H
(shortest possible three: Feb-Apr, longest possible three: Nov-Jan or Jun-Aug or Jul-Sep)
● PT2880H = P120D ≤ P4M ≤ P123D = PT2952H ; P4M ordered as: PT10518984S = PT2921,940H
(shortest possible four: Jan-Apr or Feb-May or Nov-Feb, longest possible four: May-Aug or Jul-Oct or Oct-Jan)
● PT3600H = P150D ≤ P5M ≤ P153D = PT3672H ; P5M ordered as: PT13148730S = PT3652,425H
(shortest possible five: Feb-Jun, longest possible five: any range not including Feb)
● PT4344H = P181D ≤ P6M ≤ P184D = PT4416H ; P6M ordered as: PT15778476S = PT4382,910H
(shortest possible six: Jan-Jun or Feb-Jul, longest possible six: Jul-Dec or Aug-Jan)
● PT5088H = P212D ≤ P7M ≤ P215D = PT5160H ; P7M ordered as: PT18408222S = PT5113,395H
(shortest possible seven: any range including Feb, longest possible seven: Jul-Jan)
● PT5808H = P242D ≤ P8M ≤ P245D = PT5880H ; P8M ordered as: PT21037968S = PT5843,880H
(shortest possible eight: Feb-Sep or Sep-Apr or Nov-Jun, longest possible eight: May-Dec or Jun-Jan)
● PT6552H = P273D ≤ P9M ≤ P276D = PT6624H ; P9M ordered as: PT23667714S = PT6574,365H
(shortest possible nine: many, longest possible nine: May-Jan)
● PT7272H = P303D ≤ P10M ≤ P306D = PT7344H ; P10M ordered as: PT26297460S = PT7304,850H
(shortest possible ten: Feb-Nov or Sep-Jun, longest possible ten: Mar-Dec or Apr-Jan)
● PT8016H = P334D ≤ P11M ≤ P337D = PT8088H ; P11M ordered as: PT28927206S = PT8035,335H
(shortest possible eleven: many, longest possible eleven: Mar-Jan)
● PT8760H = P365D ≤ P12M = P1Y ≤ P366D = PT8784H ; P12M, P1Y ordered as: PT31556952S = PT8765,820H
(longest possible: leap year)
● PT35040H = P1460D ≤ P4Y ≤ P1461D = PT35064H ; P4Y ordered as: PT126227808S = PT35063,280H
(every 100 years, we skip a leap year)
● PT876576H = P36524D ≤ P100Y ≤ P36525D = PT876600H ; P100Y ordered as: PT3155695200S = PT876582,000H
(every 400 years, we skip skipping the leap year)
● PT3506328H = P400Y = P146097D = PT3506328H ; P400Y ordered as: PT12622780800S = PT3506328,000H
(this it the shortest number of months where we know exactly how many days it is)