Tag Archives: Fraud Detection

Unlocking New Uses for your SIEM: Beyond Cybersecurity

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Paul Curwell

7–11 minutes

3 key takeaways:

  1. Most companies are sitting on powerful analytics platforms like SIEMs—but rarely use them beyond cyber.
  2. There’s untapped potential to apply these tools to fraud, insider threat, IP protection, and compliance monitoring.
  3. With the right strategy, businesses can reduce compliance costs, improve visibility, and make better investment decisions.

Why this matters

Today’s risk environment demands more from businesses than ever before. Whether you’re protecting sensitive R&D, complying with complex regulations, or trying to prevent fraud, the traditional playbook is falling short. Organisations invest millions in security analytics. Frequently though, use of these tools happens in a silo, begging the question “can’t they do more?”. That’s a missed opportunity.

Many organisations already own high-powered Security Information and Event Management (SIEM) and observability platforms to give rich, real-time operational insights. In most businesses, there is no use of these tools outside of cybersecurity. That’s where this story begins.

The landscape: SIEMs, observability tools, and everything in between

The tooling landscape in most medium-to-large enterprises is complex, but it’s also full of hidden potential. From security teams running SIEMs to engineers monitoring application performance, there are multiple systems collecting, storing, and analysing valuable data. The challenge is most businesses don’t fully understand how these platforms differ—or how they complement one another.

Let’s unpack the main types of platforms:

  1. Security Information and Event Management (SIEM) – These platforms are the backbone of many security operations centres. SIEMs like Splunk, Sentinel, and Elastic collect and correlate security events to find and respond to threats in real time. They’re also critical for compliance reporting, audit trails, and forensic investigations.
  2. Observability platforms – Tools like Datadog, New Relic, and OpenTelemetry provide deep insights into how systems are operating. Used by DevOps and Site Reliability Engineers, they collect metrics and logs to monitor system health, performance, and prevent outages.
  3. Data lakes and warehouses – These centralised platforms are great for long-term storage and complex data queries. However, they often lack the speed or alerting capability needed for real-time risk response.
  4. BI dashboards and analytics tools – Platforms like Power BI and Tableau provide strong visualisation for decision-making. They focus on historical data, not real-time detection.
  5. Log management platforms – Tools like ELK store data for troubleshooting, but don’t get integrated into business processes.
  6. Application Performance Monitoring (APM) tools – Focus on user experience and technical metrics but often miss the business context needed for enterprise insights.
  7. Custom threat intelligence platforms – Powerful in capable hands, but often resource-intensive to maintain and inaccessible to non-technical teams.

Understanding how these tools work—and where they overlap—opens up new opportunities for extending their use into fraud, compliance, and continuous monitoring.

Non-cyber use cases hiding in plain sight

What became clear through my research is that many businesses are unknowingly sitting on a goldmine of data. This data can improve resilience, situational awareness and decision quality, resulting in reduced losses. Many tools already have access to the underlying telemetry. The gap is that organisations don’t translate their use cases into language or workflows these systems can use to solve business or compliance problems.

Here are a few real-world examples of how some organisations are using their existing telemetry platforms to solve non-security problems:

  • Fraud detection – One financial services firm used their SIEM to detect behavioural anomalies in user logins and transaction data. This helped identify fraudulent activity faster and reduce false positives in fraud alerts.
  • IP protection – A biotech set up observability pipeline alerts to detect unusual access patterns to protected research environments. This gave them a chance to intervene before valuable data walked out the door.
  • Insider threat monitoring – A large enterprise integrated HR systems with SIEM logs to flag when high-risk employees (e.g. those about to exit the company) accessed sensitive files, enabling pre-emptive action.
  • Physical security integration – A logistics company ingested building access logs into their SIEM to monitor for suspicious after-hours activity. This provided near real-time visibilty of threats in zones containing high-value or regulated assets.
  • Regulatory compliance – A US health services provider configured automated alerts to detect improper access to patient records. This streamlining HIPAA compliance and reporting, easing the burden on their audit teams.

These examples aren’t outliers. They represent what’s possible when organisations look beyond the traditional cyber perimeter and align technology with broader business risks.

Alert management and insider risk continuous monitoring systems

Trade-offs and tricky bits

Of course, extending the use of SIEMs and observability platforms isn’t without its challenges. These are powerful tools, but were built with specific users and functions in mind. Repurposing them for broader use requires careful planning, stakeholder alignment, and a realistic view of limitations.

MetricConsiderations
Cost vs returnSIEM platforms, in particular, can become prohibitively expensive as more data sources are added. Every additional log source or telemetry stream can drive up ingestion costs, licensing fees, and infrastructure requirements. Businesses need to balance the value of added insights against escalating costs.
Expertise and resourcingMany of these platforms are complex and require specialist skills to configure and manage. Cyber teams are often already overstretched, they don’t have capacity. Asking them to support fraud, compliance, or operational use cases often requires cross-skilling or additional resources.
Data governance and privacyAggregating sensitive business data—such as HR records, payroll, or personnel movements—can raise privacy concerns. Any use needs to be aligned with data protection laws such as Australia’s Privacy Act, or the GDPR in Europe.
Tool mismatch and workflow gapsObservability platforms are fast, lightweight, and built for performance. But they’re not designed for legal defensibility, long-term retention, or audit-ready compliance reporting. SIEMs, on the other hand, are great for that. But, they can lack the ease of use or responsiveness that observability tools provide.
Redundancy and duplicationWithout coordination, multiple teams end up collecting and analysing the same data using different tools. This can lead to inefficiency and potential confusion around ownership and accountability. Worst case for regulatory compliance, you generate contradictory records which is a red flag to an inspector.
Table: Benefits and Challenges

Yes, there are challenges, but the opportunities are too great to ignore. Now’s the time for risk and compliance leaders seeking smarter, scalable approaches to assurance to speak to the CIO.

Crafting Security Business Cases for Executive Buy-in

Real compliance benefits—if you play it right

Compliance is a growing cost centre for many organisations. Increasingly, fraud and protective security is becoming a regulated compliance program. Take Australia’s Privacy Act, Scams Protection Framework Act and Security of Critical Infrastructure Act as two examples. Teams are under pressure to meet complex compliance obligations, conduct audits, investigate incidents, and coordinate a response. Given most responses increasingly relate to compliance obligations, there’s a regulatory imperative to get this right. They’re often using manual processes and disconnected systems to do this, taking time, effort and higher chance of errors.

This is where SIEM and observability platforms can play a much bigger role. By automating key controls organisations can reduce the manual workload on compliance and audit teams. Examples include detecting access to sensitive data, validating privileged user activity, or monitoring export-controlled environments. The result? Improved productivity, cost control, and compliance. Dashboards and real-time alerts eliminate the need for manual reviews, reduce investigation time, and improve coordination across the business.

These platforms also provide strong evidence for legal and regulatory inquiries. For example, access logs and alert histories makes it easier to prove data segregation or show controls were in place. This supports compliance SOX, the Privacy Act, or Australia’s Security of Critical Infrastructure Act (SOCI).

These tools allow compliance teams to shift from reactive policing to proactive risk reduction. In turn, this makes them more efficient, more strategic, and more valuable to the business.

Developing an compliance obligation register for your business

What business leaders need to do next

This isn’t just a technology issue—it’s a business opportunity. Executives should be asking how they can leverage their existing technology investments to solve new problems.

Here’s a five-step path to get started:

  1. Audit your existing tools – Inventory the telemetry and analytics platforms already in use. Identify whether you have a SIEM, an observability platform, or both. Are you using these to good effect?
  2. Map broader risks – Work with fraud, HR, IP, and compliance stakeholders to identify high-impact, high-cost business risks. Identify use cases that benefit from automation and real-time monitoring.
  3. Engage privacy and legal early – Involving these teams from the outset. This helps prevent delays later and ensures any solution aligns with data protection laws and internal governance frameworks.
  4. Pilot a use case – Choose one low-risk, high-impact use case (e.g. unusual access to critical systems) and configure alerts or dashboards using existing tools. Track the cost, value, and effort involved.
  5. Build the business case – Quantify what value these solution will save in hours, cost or loss reduction, or productivity. Present this in a way that links directly to business strategy and financial performance.

If you’re already paying for the Ferrari, why are you only using it for trips to the supermarket? With a little tuning and creativity, you can unlock value across new use cases without buying yet another tool.

Further Reading

DISCLAIMER: All information presented on PaulCurwell.com is intended for general information purposes only. The content of PaulCurwell.com should not be considered legal or any other form of advice or opinion on any specific facts or circumstances. Readers should consult their own advisers experts or lawyers on any specific questions they may have. Any reliance placed upon PaulCurwell.com is strictly at the reader’s own risk. The views expressed by the authors are entirely their own and do not represent the views of, nor are they endorsed by, their respective employers. Refer here for full disclaimer.

Graph or Social Network Analysis – what’s the difference?

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Paul Curwell

Common terminology sows the seeds of confusion

If you’re someone who has been involved in fraud protection, Anti-Money Laundering, Counter-Proliferation, Sanctions Evasion, anticounterfeiting (the list goes on) – basically any sort of investigation of networks, you will likely have come across concepts such as graph, link analysis, and network analysis. However, when you start to write use cases for your organisation and develop your functional requirements for technology, this starts to get messy. For those new to this area, the figure below provides an illustration of what social network analysis is:

Illustration of a social network in analyst notebook
Social Network Analysis illustration, US Dept. of Justice (2016)

Unfortunately, the terminology we use every day is the source of much confusion amongst business users (investigators, intelligence analysts, security & fraud professionals), data scientists and technologists alike, making it hard to understand the actual problem which needs to be solved by technology. To understand this space, there are three main concepts to get your head around:

  • Network Analytics: Is a term that has its origins in computer science and ICT, and is used to help model, monitor and assess the health and performance of computer networks
  • Graph Analytics: Also known as ‘Graph Technology’, this term actually refers to a type of database – the Graph Database – which stores data in the form of a ‘graph’ or network. Graph is heavily used today in the newly emerged field of Data Science.
  • Social Network Analysis: Also known as ‘link analysis’, ‘network analysis’, and a variety of other names, this methodology has been around since the 1970’s and stems from the social sciences. It uses algorithms and other methods to model and depict the behaviours of groups of entities (e.g. people, objects), attributes (e.g. the characteristics of objects, such as a person’s name), and the relationships (connections) between them. This is important as Entities typically exist as ‘networks’ in society.

The three concepts outlined above, each a distinct academic discipline, can be applied to three simple User Personas, as outlined below:

UserUse Case
IT DepartmentsUse network analytics to assess and manage the health of your IT and OT (operational technology – such as SCADA systems) networks
Data Scientists, Data EngineersUse Graph Databases to facilitate complex modelling, analysis, and other data management related tasks
Intelligence Analytsts, Investigators, Risk & Compliance OfficersPerform social network analysis to understand threat networks, such as criminal networks, organised fraud syndicates, or illicit corporate structures to assist in their identification, targeting and disruption
Three illustrative user personas for graph and social network analysis

Despite often using terminology interachangeably, we are actually referring to three distinct concepts which cause confusion when co-mingled.

What is a graph exactly?

A basic graph – whether we are talking about the way data is visualised within a graph database or as part of social network analysis – is depicted by nodes (entities) and edges (links or relationships). Fraud teams use enhanced depictions of ‘graphs’ to enrich a data with more information. Graphs (social networks) can be queried to return matching results, such as showing all individuals who are connected to a specific address in some way (e.g. home, work, family connections).

For data scientists, one attractiveness of a graph database is that large networks can be more efficiently searched or analysed compared to a Relational Database (RDBM) such as SQL Server or Teradata. There are numerous use cases for graph databases, including:

  • Entity Resolution – to determine whether two entities are actually the same based on various attributes
  • Knowledge Graphs – to help answer questions or find the answer to something
  • Product Recommendation Engines – for customers of eCommerce stores to suggest other products purchased by similar customers
  • Master Data Management
  • ICT network infrastructure monitoring
  • Fraud detection

Examples of graph databases on the market today include those produced by Neo4j, TigerGraph, AWS Neptune, Microsoft Cosmos, and many others.

Why is Social Network Analysis important for countering threat networks?

The term “Threat Network” is used by the U.S. Government when discussing any type of hostile actor (even lone actors are typically part of some social network). Examples include organised crime, nation states, organised fraud syndicates, counterfeiting syndicates, and industrial espionage networks. Without going into too much detail here, every threat network has a number of common roles which are required to achieve its objective.

Let’s say a consumer fraud ring is running a boilerroom scam to defraud elderly investors. The network needs people to manage its finances, communications, recruitment, targeting to spot vulnerable investors, scammers to actually defraud them, and managers and leaders to coordinate the scheme. This concept is illustrated below in relation to drug production and trafficking:

Organisational structure showing roles within a typical organised crime network
Illustration of various roles within a threat network (JP 3-25)

Social Network Analysis allows for visualisation of relationships and structures of all parties involved in the network, providing the ability to overlay additional information such as functions in the network. Social Science algorithms, such as Betweenness and Centrality, can be applied to social network data to identify key players or connections. These threat network vulnerabilities can then be targeted, such as through arrests or new internal controls, to disrupt threat actor activites. This concept is illustrated below:

Illustration of how a network can be disbanded (disrupted) with effective targeting
Illustration of how disrupting a network can render it ineffective (JP 3-25)

How can I perform Social Network Analysis?

Interestingly, you do not need a ‘graph database’ to perform Social Network Analysis. What you do need though is a suitable user interface for business users (e.g. investigators) which allows them to query, analyse, and interact with their data to achieve an outcome – such as identifying key players in a fraud ring. Without a suitable interface, business users will be unable to exploit the data effectively rendering it useless.

Fraud and law enforcement teams have used Social Network Analysis for decades. You can do simple Social Network Analysis on paper or a whiteboard without the use of software – this is where the term ‘link analysis’ originated from. Whilst pinboards are useful for Hollywood movies and simple networks, analysts today are swamped in data making software essential.

man in gray long sleeve suit holding a pen - social network analysis with paper and a pinboard
Photo by cottonbro studio on Pexels.com

In the late 1990’s or early 2000’s, the popular software known as Analyst Notebook was developed and is still in use today. These days, there is a proliferation of thick client and browser based software which performs this function, including Maltego, Linkurious, Palantir, Quantexa, and RipJar.

As outlined here, there is a distinct difference between the concepts of network analysis, graph and social network analysis. Each has its own use cases, methodologies, user groups and supporting software. Understanding this landscape, and how all the pieces fit together, is essential to building any sort of threat intelligence or detection analytics capability.

Further Reading

DISCLAIMER: All information presented on ForewarnedBlog is intended for general information purposes only. The content of ForewarnedBlog should not be considered legal or any other form of advice or opinion on any specific facts or circumstances. Readers should consult their own advisers experts or lawyers on any specific questions they may have. Any reliance placed upon ForewarnedBlog is strictly at the reader’s own risk. The views expressed by the authors are entirely their own and do not represent the views of, nor are they endorsed by, their respective employers. Refer here for full disclaimer.

What are the main e-Commerce frauds targeting online stores?

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Paul Curwell

Three challenges in eCommerce Fraud Protection

One of my side hustles is lecturing postgraduate university students on financial crime intelligence – this is all about how to identify and detect fraud and illicit activity in your data. I regularly tell my students (and clients) that fraud is really a ‘process-based crime’ – it arises because of internal control gaps in your business processes which equate to vulnerabilities for your business, and opportunities for fraudsters and criminals.

shoes in boxes on shelf
Photo by Stanislav Kondratiev on Pexels.com

Different types of fraud arise at different points in the eCommerce process. Every fraud scheme has its own unique characteristics, which means we can prevent and detect it! From my perspective, there are three challenges in eCommerce fraud protection:

  1. Detecting customer profiles or transactions which are highly likely to be fraudulent with a low false positive rate (see here for explanation); and,
  2. Detecting the fraud in time to avoid incurring a loss (this is particularly hard with realtime payments, outourced and / or automated fulfilment); and,
  3. Striking the right balance between enough loss prevention measures to mitigate the risk (your ‘risk appetite’) and too many controls (which makes for a bad customer experience, impacting sales conversions and customer retention).

To illustrate this for eCommerce, I have used the four-phased eCommerce marketing lifecycle promoted by SmartInsights.com and overlaid where different fraud schemes can arise:

Three categories of eCommerce fraud schemes

Let’s deep dive into the three main eCommerce fraud schemes:

Account related frauds

Some eCommerce fraud schemes revolve around a users identity or account. Examples of ways in which this may happen, either at account creation or account login include:

  • Phishing – social engineering attempts to compromise users and their accounts
  • Credential stuffing – attempts to use credentials stolen from another breach to login
  • Account takeover – where a user’s account credentials or browser session is hijacked
  • Identity theft – a victim’s identity is stolen and used to obtain loans, goods, etc.

Payment Frauds

The second category of eCommerce frauds revolves around the payment or transaction itself, including:

  • Use of stolen / purchased credit card details
  • Card testing – where criminals place small charges on a card to see if it is valid which could be disputed by the cardholder
  • Chargeback fraud – shopper makes a purchase on their own card, then requests a chargeback after receiving the goods
  • Refund Scams – shopper purchases something and ask for a refund before the product is delivered
  • Payment frauds – including card present and card not present transactions
black payment terminal
Photo by energepic.com on Pexels.com

Loss Prevention

The final category of eCommerce frauds is perpetrated by a user post-payment. Common fraud typologies include:

  • Change of address scams – delivery address is changed after payment but before shipping so goods are not sent to cardholders residence
  • Returns fraud – consumer receives goods, uses it, and sends it back (effectively ‘renting’)
  • Product diversion – where goods are basically stolen by trusted insiders (employers, contractors, suppliers)

Did you know that organised fraud, product diverters and shoplifting rings typically target specific products over others?

Products that are CRAVED are at greatest risk.

I have provided more information on which products are most likely to be targeted by organised fraud, product diversion and shoplifting rings in my article “product security risk assessments for tangible goods”.

Product security risk assessments for tangible goods

Identifying your core business activities, systems and processes is key to understanding and managing your risk profile. I will review how to do this in a future article, but if you are looking for somewhere to start try www.juliantalbot.com and this article on ‘risk appetite and risk tolerance‘.

Further Reading

DISCLAIMER: All information presented on ForewarnedBlog is intended for general information purposes only. The content of ForewarnedBlog should not be considered legal or any other form of advice or opinion on any specific facts or circumstances. Readers should consult their own advisers experts or lawyers on any specific questions they may have. Any reliance placed upon ForewarnedBlog is strictly at the reader’s own risk. The views expressed by the authors are entirely their own and do not represent the views of, nor are they endorsed by, their respective employers. Refer here for full disclaimer.

Comparative Case Analysis: A powerful tool for typology development

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Paul Curwell

What is Comparative Case Analysis?

Comparative Case Analysis (‘CCA’), also known as ‘Similar Fact Analysis’, is a technique used in criminal intelligence analysis to identify similarities and support decision making (Sacha et al, 2017).

Cases can be linked in CCA through any of the following:

a) Modus Operandi (or tactics, techniques, procedures)
b) Signatures and patterns
c) Forensic evidence
d) Intelligence

College of Policing (2023), United Kingdom

CCA is useful when analysing process-based crime types where perpetrators need to follow a defined set of steps to effect the crime. Examples of such crime types include fraud and financial crime, cybercrime, money laundering and Intellectual Property Crime (e.g. counterfeiting networks).

I use CCA when developing typologies, which I then convert to analytics-based detection models which are run as part of a continuous monitoring or detection program over a dataset to detect suspect transactions, individuals/ legal entities, or behaviour.

a person pointing on to the photographs
Photo by RODNAE Productions on Pexels.com

Where can you collect cases to perform CCA?

So, you’ve worked out that CCA is appropriate to use in your situation. The next challenge is where to get your case study data from. Common sources include:

  • Indictments and statements of claim – depending on jurisdiction, these may be published by prosecutorial agencies such as the U.S. Department of Justice, or by the courts (for tips, see my article on searching Australian court records).
  • Media reports – media monitoring and other Open Source Intelligence (OSINT) capabilities are essential for any financial crime or corporate security function. For information on how to build one, look at my 101 post.
  • Industry information sharing sessions – industry groups such as the Pharmaceutical Security Institute and the Australian Financial Crimes Exchange exist for this purpose.
  • Prisoner interviews – may be performed by law enforcement, regulators, journalists or academics for publication.
  • Academic case studies, published papers and conferences
  • Examination of your own case files based on historical incidents or near-misses.

Unfortunately, it is all too common to find cases that are incomplete. If you don’t control your data (such as cases sourced from the media) your ability to improve data quality is limited – you may need to exclude incomplete cases from the CCA.

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If you are using your own case files, consider changing your internal processes, templates and SOPs to collect the data you need in the future. If you encounter resistance, obtain buy-in from stakeholders by helping them understand what you need and why you need it.

How do you undertake Comparative Case Analysis?

CCA is an invaluable but involved process which will take time to complete. CCA has its roots in academia, particularly the social sciences (see Goodrick et al 2014), so some literature on the topic is irrelevant or too academic to be useful for typology development or intelligence analysis.

photo of women laughing
Photo by RF._.studio on Pexels.com

CCA can be undertaken individually or within a group, although doing the work individually may lead to intelligence blindspots. My high level methodology is as follows:

StepTaskConsiderations
1Define your scope, case criteria, and other considerations a) What are you attempting to achieve by performing this CCA? Is CCA the most appropriate method?
b) What risk are you seeking to mitigate and what type of case / crime type etc meets these criteria?
c) What timeframe, jurisdiction, industry / product / channel / customer type are in scope?
d) How might analytical bias arise in your methodology? How will you manage this?
2Collect your case information and prepare the data for analysisa) Refer to the ‘where can you collect cases to perform CCA?’ for suggestions
3Review each case for data quality and completenessa) Do you have sufficient information for each case?
b) Do your cases fit the criteria you defined in step 1?
c) Do you need to change your methodology?
d) Is the methodology viable with the avilable information?
e) What cases (if any) do you need to remove due to incomplete data?
4Develop a structured form or methodology to undertake the comparisona) How are you going to compare each case? I build a form or template as part of my approach which I populate with information from each case and use this for case comparison
b) What data elements do you want to compare? Details captured usually include entities (people, businesses, things such as vehicles or residences), locations and dates / times, activities (e.g. events, transactions), and attributes such as language in addition to Modus Operandi.
c) Comparison of this data enables the identification of patterns or attributes which can be used to link seemingly separate incidents together (remember criminals share with each other, a liked case doesn’t have to reflect the same individual).
5Determine where you will store your resultsa) Where will you store your captured data and analysis?
b) If dealing with large volumes of data, you may want to build a database or design a workbook in Microsoft Excel to collect the data for subsequent analysis.
6Read each case and identify each data elementa) Physically read the material for each case
b) Identify the data elements which you want to capture (step 4). One way to do this is using coloured pens or highlighters, with each colour representing a specific data element (e.g. entities).
c) Once identified, this information can be used to document your results (step 7)
7Document your resultsa) I tend to find Microsoft Word, PowerPoint or Excel is fine for this purpose, but ensure you store your CCA reports in a central location so they can be peridocially reviewed and updated.
b) An alternative is ‘visual CCA’, effectively using a visualisation tool such as Tableau or Microsoft PowerBI to analyse and present your findings (see Sacha et al 2017)
c) Ensure any assumptions, data gaps or hypotheses are clearly identified (ideally CCA is factual, so if there are information gaps you are better off leaving this blank than filling a gap with a hypothesis. The fact you have done this can get overlooked in future typology and detection model work and lead to erroneous results).
8Have an ‘independent party’ peer review or critique your worka) Having another party (e.g. team or peers, independent experts etc) not involved in original activity perform a review and challenge role.
b) This provides an opportunity to identify gaps, assumptions or conclusions in your analysis.
9Evaluate your results a) Are they complete?
b) How reliable do you think they are?
c) Are they sufficiently detailed and rigorous enough to use as a basis for typology development?
d) What if any rework do you need to do before finalising your CCA? Perform updates to your work as appropriate.
10Periodically refresh completed CCAsa) Threats such as fraud, financial crime and cybercrime are constantly changing in response to new processes, products, channels, internal controls and actions taken by fraud and security teams to mitigate these threats.
b) Implement a process to periodically reivew and update historical CCA, such as annually, and incorporate this into any detailed typologies.
Paul Curwell (2023). Comparative Case Analysis methodology, http://www.forewarnedblog.com

A simplified example of a CCA data capture template (step 4) which has been populated with fictional case information (steps 6 and 7) is shown below:

A simplified example of a CCA data capture template (step 4) which has been populated with fictional case information (steps 6 and 7).

Typology development: the next step in operationalising detection

Whilst CCA is not a pre-requisite to developing a typology, it certainly helps. When designing your CCA approach, I recommend you consider the types of data you will need to build your typology and incorporate these into your methodology (see my previous article, ‘typologies demystified‘).

Analysing Modus Operandi or TTPs requires the application of a number of intelligence analysis methods and is too big to cover here. I will write about this separately in a future post.

Further Reading

DISCLAIMER: All information presented on ForewarnedBlog is intended for general information purposes only. The content of ForewarnedBlog should not be considered legal or any other form of advice or opinion on any specific facts or circumstances. Readers should consult their own advisers experts or lawyers on any specific questions they may have. Any reliance placed upon ForewarnedBlog is strictly at the reader’s own risk. The views expressed by the authors are entirely their own and do not represent the views of, nor are they endorsed by, their respective employers. Refer here for full disclaimer.