Enhancing Human Decisions with AI-Powered Insights

Artificial intelligence has enabled us to process and interpret vast amounts of online content more efficiently than ever before in order to make critical decisions based on accurate analysis. By integrating advanced capabilities like generative AI, post categorization, and Named Entity Recognition (NER), Signal’s tools are designed to amplify human expertise, not replace it.

Streamlining Content Categorization

The sheer volume of digital content produced every second makes it increasingly difficult for analysts to identify actionable information. AI can help bridge this gap by recognizing threats of violence, hate speech, and a myriad of other areas of concern and then tagging it for a human analyst’s attention.

Instead of manually sorting through thousands of posts, analysts can rely on these systems to surface what matters most, cutting down noise and focusing their efforts where it’s needed. This level of automation ensures no critical detail slips through the cracks, even during high-pressure scenarios.

At Signal, categorization doesn’t stop at basic filtering. Our technology is designed with analysts' needs in mind, using machine learning models trained on real-world data. These models adapt to recognize the nuances of language and context, whether it’s a vague online threat or coded messages from a particular online community.

By grouping relevant posts together under tailored categories, we help analysts build a comprehensive understanding of any situation in a fraction of the time.

Connecting the Dots with Named Entity Recognition (NER)

Manually identifying key details like names, locations, and organizations across a sea of information is both time-consuming and error-prone. With NER, AI can instantly extract these critical elements from posts, offering a structured overview of the key players and locations involved. This feature enables analysts to see connections and patterns that might otherwise go unnoticed, giving them a head start on piecing together a full narrative.

NER is especially valuable in chaotic situations where details are emerging rapidly. For example, during a breaking news event, analysts can use this capability to identify recurring names or places being mentioned online.

This doesn’t just save time: it creates a foundation for deeper investigations, helping analysts connect information across platforms, conversations, or even geographical areas.

Empowering Analysts with Generative AI

Report writing is a core part of an analyst’s job, but it’s also one of the most time-intensive tasks. Generative AI transforms this process by helping draft initial reports in a polished, professional style. Analysts can input key details and receive a draft that’s ready for refinement, significantly reducing the time between gathering insights and delivering findings to decision-makers.

This capability doesn’t just streamline operations—it improves the quality of reports, too. By automating the more routine aspects of writing, analysts can focus on crafting more thoughtful conclusions or verifying critical details. Whether it’s summarizing complex datasets or generating readable summaries of dense information, generative AI ensures analysts spend their time where it counts: interpreting data and making assessments.

Uniting Fragmented Information

When incidents unfold online, they’re rarely confined to a single post or source. Discussions emerge across platforms, each contributing a piece of the puzzle. Signal’s AI clusters related posts to give analysts a complete, unified view of any event. This capability is particularly important for understanding fast-evolving situations, where isolated snippets of information need to be pieced together into a coherent narrative.

The Global Feed feature - providing next-generation open-source intelligence - takes this even further by providing access to a broad range of publicly available data in real time. By clustering posts and analyzing them collectively, analysts can uncover trends, track the spread of misinformation, or identify emerging threats. These insights are critical for producing reports that don’t just summarize events but also offer context and actionable recommendations.

Actionable Workflows, Timely Outputs

In time-sensitive situations, delays are catastrophic. Signal’s AI tools are built to prioritize speed and accuracy, automating repetitive tasks like post collection, categorization, and clustering. This ensures that workflows remain streamlined and decision-makers receive timely insights to guide their actions.

The impact of timely outputs extends beyond efficiency; it directly influences how decisions are made. Whether it’s responding to a security threat or planning a public relations strategy, actionable intelligence delivered in real time allows teams to act with confidence. Signal’s technology ensures that analysts can keep pace with the speed of the internet, empowering them to deliver insights that matter when they matter most.

AI as an Enabler, Never a Replacer

AI’s potential is transformative, but it’s no substitute for the critical thinking, intuition, and experience that human analysts bring to the table. Tools like Signal are designed to complement—not compete with—human expertise. By automating the most time-intensive tasks, AI enables analysts to focus on higher-value activities, such as interpreting ambiguous data or assessing the nuance of a potential threat.

The human-in-the-loop approach is particularly vital in complex cases, such as assessing threats or identifying patterns that require deeper contextual understanding. While AI provides the tools to speed up workflows and surface critical insights, it’s the analyst’s role to ensure that these insights translate into meaningful actions. At Signal, we believe the best results come from the perfect balance of technology and human expertise.

Try Signal

Want to see these capabilities in action? Request a demo today and discover how Signal’s Global Feed and AI-driven tools can transform your workflow.

Why Transparency is Critical in the Era of 'Black Box' OSINT Solutions

The allure of “one-click magic” solutions is undeniable. A tool that promises comprehensive results at the press of a button? Great. No digging, no deliberating, just answers. It sounds like a dream, doesn’t it? But dreams can quickly turn into nightmares when the methods behind those answers are shrouded in mystery.

As the old saying goes: if it sounds too good to be true, it probably is.

As we move further into an era dominated by artificial intelligence, it is imperative for analysts to demand transparency from “black box” OSINT solutions.

The Hidden Risks of Black Box OSINT

Without a clear understanding of how intelligence results are derived, users are left with little more than blind faith. Consider the consequences in high-stakes industries like journalism, law enforcement, or national security, where a single unverified piece of information could lead to reputational damage, operational failures, and even endanger lives.

Moreover, the very nature of these tools reduces trust in AI-driven solutions. When users are unable to see how conclusions are reached, skepticism grows. This lack of confidence undermines the potential of artificial intelligence to assist in critical decision-making, turning what should be a powerful ally into a questionable crutch.

Users should never be in the dark about the mechanics of their tools. A lack of transparency not only risks operational credibility but also perpetuates the idea that OSINT solutions are “magic” rather than reliable, verifiable systems.

A Beacon of Transparency: the Power of Clear Sourcing

Rather than hiding behind proprietary algorithms and secretive processes, Signal’s Global Feed platform provides users with interactive dashboards and traceable data points, making it easier to cross-verify intelligence. This proactive transparency is a game changer in an industry plagued by ambiguity.

Transparency begins with an honest discussion of AI’s capabilities and limitations. Global Feed doesn’t sell illusions. Instead, it equips users with a clear picture of what AI can achieve, alongside its potential pitfalls. This openness allows users to navigate the complexities of OSINT with confidence, rather than uncertainty.

Global Feed also incorporates the Admiralty Scale, a trusted method from the intelligence community, to evaluate the confidence and credibility of its sources. This approach not only ensures accuracy but also fosters a deeper understanding of the data’s nuances.

Why Transparency Fosters Trust

Trust is the currency of effective intelligence, and transparency is its foundation. But trust doesn’t come from blind faith; it’s earned through understanding. Global Feed recognizes this and prioritizes user awareness at every step.

By providing clarity and openness on its methodologies, Global Feed demystifies the process of AI-driven intelligence. Users don’t need to be experts in machine learning to grasp the basics of how the platform works. This accessibility empowers users to make informed decisions, rather than relying on the supposed infallibility of a machine.

This transparency creates an environment where users can not only trust their tools but also feel empowered to justify their decisions to stakeholders. The combination of clear sourcing, intuitive tools, and ethical AI use sets a new standard for OSINT platforms.

The Future of OSINT Lies in the Open

The world of OSINT is at a crossroads. On one side, we have black-box solutions that promise simplicity but deliver opacity. On the other, transparent tools like Global Feed that embrace openness as a guiding principle. As the demand for ethical AI grows, it’s clear which path will prevail.

Transparency isn’t just a buzzword; it’s a necessity. It’s the difference between tools that merely function and those that truly empower.

Choose Signal’s Global Feed

You can place your trust in tools that guard their secrets, or you can opt for solutions that place their trust in you by being transparent and forthright. The era of blind faith in “one-click magic” is over. It’s time to demand transparency. And with Global Feed, that demand is met honestly and upfront.

Choose transparency. Choose trust. Choose Global Feed.

The Role of AI in OSINT: Introducing New Briefer Tool

Open source intelligence (OSINT) has transformed in recent years, following the explosion of data and the introduction of artificial intelligence (AI), effectively hallmarking a new era of security support solutions.

AI-driven OSINT tools excel in collecting and analysing publicly available information from diverse online sources. We’re excited to be at the forefront of this new era, announcing a new feature within our latest version of Signal, Briefer, designed to reduce repetitive tasks for security teams, streamline processes and make hunting out threats faster and easier.

Artificial intelligence and open source, a powerful match

There is a  growing demand for solutions that improve efficiency and enable teams to quickly track and research vast data across the web and social media. Top players in the market have been identified as those that can provide expert analysis and interpretation of the data they collect, identifying patterns and trends, and sifting genuine threats from noise.

In addition, while AI and machine learning has been used in security processes for more than a decade, with the rise of generative AI and tools such as ChatGPT, AI has reached a new level of usability of everyday security staff, with clever and widely functional use cases showcasing the power of the technology.

In our digital age, AI models are now able to meet the scale of the open data landscape, so instead of an insurmountable challenge it becomes a goldmine of information to be analysed. As a result, security teams are greatly empowered, and the business and its people benefit from being one step ahead of threats.

Breaking down the role of AI in OSINT, and introducing Briefer

As highlighted by Oxford Internet Institute, “AI can also assist the analysis phase of the OSINT cycle, generating valuable intelligence based on pre-trained models and thus countering the information overload problem faced by intelligence analysts.”

With the help of AI, pages upon pages of public web information can be scoured, as the intelligent solution sifts through mentions, news and opinions to flag potential threats, sharing a selection of alerts with a human team. 

In addition, in the case of Signal’s new tool Briefer, reporting becomes simplified and more consistent. The AI algorithm enables teams to select various components for their report, and create it instantly for key stakeholders.

At its core, AI is improving OSINT in three ways. That is, improving data collection, developing data analysis, and generating actionable insights. This includes assisting in the prioritisation of data, correlating relevant information, reviewing raw information, and deriving relevant insights with key tags so human analysts can sift through alerts.

As we are already seeing, emerging technologies, including AI, don’t replace the role of the security team, but have the potential to greatly enhance it, saving time throughout the whole process, from hunting for threats to reporting on potential disturbances.

A new phase of security intelligence

Launching Briefer into Signal’s security solution is an exciting next step for the platform, and it highlights the budding potential of where AI is headed and where it is today.

It also shows how AI-enhanced OSINT can become an integral security resource, not to replace human analysts, but provide a foundation of efficiency and insight so they can focus more time and attention on securing the business and its people.

Review our latest product updates and book in for a free demo.

Threat intelligence climbs into the billions - and a special 10-year anniversary update

The need for threat intelligence solutions continues to rise as security leaders need to sift threats from noise and increasingly safeguard their organisation and people from online threats.

At Signal, after a decade providing leading threat intelligence solutions to a wide range of industries, we’ve seen the genuine impact digital risk management can offer in supporting the safety, security, and operational resilience of organizations.

Whether in banking, healthcare, insurance, utilities, manufacturing or entertainment, being able to monitor online data, including the dark web, can go a long way in identifying any threats to your people or daily operations, and respond quickly as needed. As technology advances so can our means of stopping threats in their tracks.

Threat intelligence market as booming market - and we can see why

In 2022, the global threat intelligence market Fortune Business Insights size is expected to experience a boom, reaching US$18.11 billion (NZ$30.6 billion).

During this forecast period, the Asia Pacific market is expected to grow significantly as security leaders seek to enhance their resilience.

Our threat environment is more complex and dynamic than ever before – rising geo-political tensions, devastating natural disasters, cyber-attacks, and the growing threat posed by violent extremism and a multitude of other drivers presents security leaders with significant challenges. Particularly amidst growing talent and skill shortages leaving security teams time poor and burnt out.

On a positive note, artificial intelligence (AI) is greatly advancing organisational resilience, particularly through centralising threat intelligence. In fact, recent market research has found that 69% of enterprises see AI as a necessary investment for responding to cyber-attacks, and 51% of executives see cyber threat detection as the use primary case for AI.

With effective threat intelligence that leverages emerging technologies, businesses can make use of one platform to monitor threat data from a wide array of data sources and formats. They can aggregate all relevant data from across the web and make it easily understandable, vastly improving the ability to mitigate risk.

Responding to a growing need - introducing Signal Version 4

Over our decade in the threat intelligence industry, we’ve always been user-led. As part of our commitment to Signal users, we’ve placed the majority of our focus and resources into providing updates and solutions to our platform that are in direct response to how people are using our services and tools.

Signal Version 4 is a culmination of months of deep-diving into how we can best improve and advance our services to cater for our modern, digital, and increasingly AI-driven business world.

This is a major update to the existing service, and also provides a new foundation to allow us to build out our powerful AI functionality, along with ongoing full-service updates. Behind the scenes we’ve been building, testing, reiterating and smoothing out both the underlying solution infrastructure and the topline view, to ensure utmost resiliency and usability.

While we’re excited for our users, new and existing, to try it out themselves, we’re most excited about our AI additions, improved user interface, data visualisation capability, customisable dashboards, and global event detection and alerting.

While security teams are up against notable challenges, with clever, user-led threat intelligence tooling we can prepare for the worst, catch risks that arise, and respond in kind.

Review the latest updates and book in for a free demo.

How can 4chan be Used as a Data Source for Security Intelligence?

What is 4chan?

4chan is one of the largest English language based image boards on the open web. They have over 900k new posts per day and some 27 million active monthly users.

What makes it a unique social platform is that users can choose to remain anonymous. They don’t even need to create an account to access and engage with content on the platform. As an added security measures, posts time-out after a period so they can’t, unless found and archived by an independent data gathering source, be checked and referenced by security teams, users, or law enforcement at later date.

There is a wide range of topics hosted on the platform, from Japanese culture, to politics, to adult content. Because of the anonymity allowed, as well as a very limited moderation by the site owners, 4chan has a large amount of illicit content and activity. This activity includes cyberbullying, child pornography, harassment, violent threats, racism and extremist ideologies. 

Despite this, it’s important to note that there is nothing inherently bad about the platform, just as there is nothing inherently bad about the dark web, and many of the users use it for legitimate purposes such as for its original intent which is the exploration and discussion of the Japanese film and television animation style, anime. In fact, like Reddit, many influential memes have originated from the platform such as ‘lolcats’ and ‘chocolate rain’. As such it has historically been an important driving force behind the development of internet culture.

Bottom line: 4chan is a forum, the original purpose was for the discussion of anime and Japanese culture. It’s a forum where users don’t need to have an account or sign up with a name, there is little moderation, and posts are deleted from the server after a period, as such there are few consequences. With this format, people can and do say just about anything on the platform.

How can 4chan benefit your organization as a data source?

The anonymity offered by the channel means users feel comfortable talking openly and they do so around a wide range of subject matters and people. For example, under the political forum /pol/ you can find examples of alt-right groups, threats of violence against a person, organization or group, and racist behaviour.

The range of topics discussed and the freedom with which individuals and groups openly discuss them can give security teams and law enforcement an idea of emerging trends as well as be one of the first places that death threats or threats of violence against individuals and organizations can be found. In the past, terrorist manifestos have also been posted on the channel. These discussions can indicate when an event is going to evolve into a tangible threat and give security teams a heads up to prepare and mitigate the potential threat.

Additionally, there are groups and individuals on the channel, such as the members of Anonymous who have turned their online attention, for right or wrong, to intelligence gathering on people and organizations. Often, 4chan is one of the first places they share their findings. For example, they might uncover and share personal details about a CEO such as their address, medical records and details of their family.

Why use Signal for monitoring 4chan?

One of the key problems with any form of online intelligence reconnaissance is the quantities of data you need to assess to get even the smallest tidbits of potentially useful information. 4chan has this problem in spades with over 3.5 billion posts. And these posts are transient, with the more R rated the post being the shorter it’s existence. This means relevant security intelligence on the 4chan platform might only be public for a couple of days. To gain real insights into the channel you need to be constantly and efficiently monitoring with real-time alerts.

4chan is just one of the data sources you can monitor using Signal though. You can simultaneously monitor the open, deep, and dark web including forums like Reddit and chatrooms like Telegram. Our system allows you to create tailored keyword-driven searches with boolean logic which is assisted by our machine learning and language processing AI to efficiently gain intel on hyper-relevant, new and emerging threats.

How Machine Learning is Changing Modern Security Intelligence

A key challenge facing modern security teams is the explosion of new potential threats, both cyber and physical, and the speed with which new exploits are taken advantage of. Additionally, in our globalized world threats can evolve from innumerable sources and manifest as a diverse range of hazards.

Because of this, security teams need to efficiently utilize automation technology and machine learning to identify threats as or even before they emerge if they want to mitigate risks or prevent attacks.

Artificial Intelligence in the Cyber Security Arms Race

Today, AI and machine learning play active roles on both sides of the cybersecurity struggle, enabling both attackers and defenders to operate at new magnitudes of speed and scale.

When thinking about the role of machine learning for corporate security and determining the need, you first need to understand how it is already being used for adversarial applications. For example, machine learning algorithms are being used to implement massive spear-phishing campaigns. Attackers harvest data through hacks and open-source intelligence (OSINT) and then can deploy ‘intelligent’ social engineering strategies with relatively high success rate. Often this can be largely automated which ultimately allows previously unseen volumes of attack to be deployed with very little effort.

Another key example, a strategy that has been growing in popularity as the technology evolves, making it both more effective and harder to prevent, is Deepfake attacks. This uses AI to mimic voice and appearance in audio and video files. This is a relatively new branch of attack in the spread of disinformation and can be harnessed to devastating effect. For example, there are serious fears of the influence they may bring to significant future political events such as the 2020 US Presidential Election.

These are just two of the more obvious strategies currently being implemented in a widespread fashion by threat actors. AI supported cyberattacks though have the potential to go much further. IBM’s DeepLocker, for example, describes an entirely new class of malware in which AI models can be used to disguise malware, carrying it as a ‘payload’ to be launched when specific criteria are met - for example, facial recognition of its target.

Managing Data Volumes

One of the primary and critical uses of AI for security professionals is managing data volumes. In fact, in Capgemini’s 2019 cybersecurity report 61% of organizations acknowledged that they would not be able to identify critical threats without AI because of the quantities of data it is necessary to analyze.

“Machine learning can be used as a ‘first pass’, to bring the probable relevant posts up to the top and push the irrelevant ones to the bottom. The relevant posts for any organization are typically less than 0.1% of the total mass of incoming messages, so efficient culling is necessary for the timely retrieval of the relevant ones.” - Thomas Bevan, Head Data Scientist at Signal.

Without the assistance of advanced automation softwares and AI, it becomes impossible to make timely decisions - impossible to detect anomalous activity. The result of which is that those organizations who don’t employ AI and automation softwares for intelligence gathering often miss critical threats or only discover them when it’s too late.

Signal OSINT and Machine Learning

Signal OSINT platform uses machine learning and automation techniques to improve data collection and aggregation. The platform allows you to create targeted searches using Boolean logic, but it is our machine learning capabilities which allow us to go beyond Boolean keyword searches. 

“By recognising patterns in speech and relations between commonly used words, one can find examples of relevant posts even without keywords. While phrases like ‘I’m gonna kill the boss’ can be picked up by keywords easily, keyword searches alone struggle with more idiomatic speech like, ‘I’m gonna put the boss six feet under’, and will incorrectly flag posts like ‘Check out the new glory kill animation on the final boss’. Machine learning, given the right training data, can be taught to handle these sorts of examples,” says Thomas Bevan.

Signal continuously scans the surface, deep, and dark web and has customizable SMS and Email alert capability so that security teams can get real-time alerts from a wide array of data sources such as Reddit, 4Chan, 8Kun etc. Additionally, Signal allows teams to monitor and gather data from dark web sources that they would otherwise be unable to access either for security reasons or because of captive portals.

Finally, the software allows users to analyze data across languages and translate posts for further human analysis. There are additional capabilities, such as our emotional analysis tool Spotlight, which can help indicate the threat level based on language indicators.

Complementing AI with Human Intelligence

In order to stay ahead of this rapidly evolving threat landscape, security professionals should be using a layered approach that pairs the strategic advantages of machine learning to parse through the vast quantities of new data with human intelligence to make up for current flaws in AI technology.

Machines have been at the forefront of security for decades now. Their role though is evolving as they get passed the heavy lifting, allowing analysts and security professionals to analyse hyper-relevant data efficiently. 

Combining Human Analysts, AI, and Automation for Fast Threat Intelligence

It is estimated that cybercrime will cost organization a combined amount of upwards of $6 trillion a year. Cybercriminals are getting smarter and to defend networks, predict threats, and protect staff, organizations need increased access to timely intelligence. 

Effective information security requires smarter detection techniques which is why many organizations are incorporating AI-driven solutions and products to enable their security teams. However, even with AI assistance the sheer amount of data to assess is encumbering. Signal offers a multi-faceted approach that incorporates filters using boolean logic, AI analysis, and a human hand.

Getting Actionable Insights in Real-Time

In threat intelligence having timely data means everything! Having hyper-relevant intelligence as or even before events are unfolding could mean the difference of several zero’s. By contrast, acting upon old threat insights that maybe have dated can be counter-productive, or even undermine the purpose of the intelligence.

Automation and AI tools can make all the difference when it comes to constantly collecting fresh data. A threat intelligence platform such as Signal which harnesses automation and AI tools massively expands the potential data sources and amount of data that an organisation is able to effectively and efficiently monitor. As well as enabling security teams to sift through all that data and detect anomalous and potentially dangerous activity.

Reacting fast is vital to mitigating threats, but what is even more effective is preempting potential attacks enabling security teams to take preventative measures. For example, using a dark web scan a security team might discover an exploit package for sale targeting a previously unknown vulnerability. Discovering this exploit pack allows the security team to patch the vulnerability before hackers have a chance to take advantage of it.

Automation isn’t Everything

Machines can save you time and in that way they save you money. The combination of AI and Automation when scanning the surface, deep and dark web allows your security team to have more eyes on more data sources. This is vitally important especially today when cyber skills are scarce and data growth so overwhelming. This combination helps prevent analysts from being utterly swamped by endless admin work and allows them to deliver true value to their role.

That being said. Machines can only do so much by themselves (at least for the foreseeable future. People remain fundamentally better at understanding insights from potentially vague context and who are able to deliver an effective response.

Acting fast as we have already mentioned is incredibly important. But just throwing machine learning at the threat intelligence problem isn’t nearly enough. The perfect blend combines rapid and large-scale initial gathering and analysis by machines that then hand-off to their human team-mates to apply strategic intellect while the data is still fresh.

Security professionals have to think how cybercriminals think: machines (e.g. botnets) to do the heavy lifting and a sprinkling of human intervention to execute as successfully as possible.

Injecting Human Intelligence into Automated Threat intelligence

The key to superior threat intelligence accuracy and timing is to leverage automation whilst simultaneously injecting human expertise. You don’t want to be wasting your human resources by making skilled data security analysts wade through piles of admin. Nor do you want those analysts to miss potential anomalous data because your automated system disregarded a seemingly meaningless information package which later turned out to be a viable threat. 

Signal allows you to create filtered searches using Boolean logic scanning your chosen data sources and understanding potential location information. These searches can additionally be run through our emotional analysis tool Spotlight. 

There is one more problem though. Getting the balance of human and automation right is essential if you want to derive an effective threat intelligence system at a competitive cost.

To solve this problem we have launched our Sapphire program. Sapphire is an optional bolt-on which enables Signal customers to leverage our skilled in-house data analysts to further refine their results allowing their in-house security personnel to spend time on delivering real value.

Final Words

As can be seen from the description above, Signal is not an “AI application” in the commonly understood way. Instead, it’s a system where we use AI techniques and automation in multiple places to create a tool which in the right hands creates an extremely capable intelligence solution.

Even though machines and software will continue to evolve with dazzling speed, the complexity of threat analysis means there will be plenty of challenging opportunities for human analysts for a very, very long time.

How to tell when negative sentiment becomes a threat to your business

There are some 500 million tweets and over 4 million new blogs posted every single day. Each of these sparks another conversation which could house potential threats against an organisation. And we haven’t yet mentioned Facebook, Instagram, Reddit, Flickr, Medium or any of the other dozens of social sites and forums where people post online. And if you thought that was a lot of noise you have to remember the dark web too, where many cybercriminals go to engage in nefarious activities with the protection of a Tor browsers anonymity. 

The point here is that the internet is full of noise. Monitoring all of that and then cutting through the noise to detect relevant potential threats requires the right tools. 

What is Sentiment Analysis?

Sentiment analysis, in short, is analysing the language in online posts and comments to determine the underlying emotion behind what has or is being posted by an individual or group.

Determining online sentiment doesn't just allow you to understand better how your brand is performing and how people feel about your business. It can also be used to manage crises and spot potential threats to assets or staff.

Without sentiment analysis, data can be misleading. Sentiment gives data extra context which allows it to be better understood enabling a more effective and accurate response to the potential rtisks. 

It also allows you to differentiate between when a negative comment is simply that, a negative comment, or when it needs more serious attention because, for example, it’s evolving into a physical threat.

Where and How do we Measure Sentiment?

Any text that gets highlighted by Signal OSINT software can be run through our sentiment analysis tool, Spotlight. This allows users to reduce the amount of noise and focus on the threats.

Sentiment can be expressed anywhere online, this might be through social media, in the comments of a blog or even in a dark web forum. Signal allows you to gather data from a huge array of open intelligence sources including (but not limited to) social media and dark web forums.

How can Sentiment Analysis Be Used for your Business?

Emerging Threats

Sentiment analysis can be an incredibly useful tool for those that wish to identify potential risks which might evolve into tangible reputational or physical threats against, employees, executives, brand or assets.

Managing Reputation

Your brand’s health and reputation are important. Having a tool that allows you to analyse the overall sentiment towards your brand and associated keywords gives organisations a bigger and better overall picture of their brand which can be a game-changer for launches of major events or analysing the success of a large marketing campaign.

Evolving Crises

When it comes to dealing with current and evolving crises having up to date and detailed situational awareness, gained through an OSINT tool such as Signal can make a huge difference. However, as we have mentioned before, there is a huge amount of noise out there. So, how do you determine which comments, which posts are relevant and need monitoring?

The answer is to use Signal to create specific filters and then run identified posts through our sentiment analysis tool “Spotlight”. This allows users to both quickly identify emerging threats and to then stay on top of these risks as they are evolving in real time.

Moving Your Marketing Forward

Social sentiment is a powerful tool for understanding the relationship between your brand, your customers, and your competitors. If you measure it regularly and act on what you learn, your team can create targeted marketing strategies to keep up with the ever-changing demands and opinions of your customers.

How do you determine when Negative Sentiment Becomes a Threat?

One of the key methods used by our software and our analysis team to tell whether or not a comment is a threat that needs more attention is the repetition of negative sentiment online by an individual or group.

For example:

• Does a particular author of a comment or post have a long history of bad-mouthing an organisation or expressing negative sentiment?

• Have they repeated the same negativity on multiple sources?

Even if they aren’t directly threatening any physical or tangible action against the organisation, if there’s enough online commentary from a single individual or group then this could escalate and it may be smart to further monitor.

You can then set up a search using our filters to target this individual or group so that you don’t miss if this negative sentiment becomes a physical or reputational threat. 

Secondly, using Spotlight, users can identify posts expressing dangerous emotions such as anger, or disappointment. Both if repeated enough should be addressed. Posts expressing anger are likely to indicate a physical threat and should be monitored for that, whilst the posts expressing disappointment may hold reputational risks. 

Summary

Sentiment analysis tools like Signal’s Spotlight can help security teams form a broader and more detailed overview of the situation to better understand the potential and emerging threats. It allows them to target their online searches and cut through the noise to identify key threats. All of this essentially means a more efficient and more effective security team.

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Critical Security Intelligence for the Financial Services Sector

Seeing in the Dark - Exposing the Dark Web

There is plenty of online information regarding the dark web – mostly accurate, although it can be daunting to understand the various nuances. There are numerous benefits that come with monitoring of the dark web.

When it comes to dark web monitoring, Signal risk intelligence software offers a comprehensive service which enables security professionals to gain increased situational awareness using targeted, highly relevant data gathered from dark web sources.

Why did we add Dark Web monitoring to Signal threat intelligence software?

The Dark Web is the place to lurk out of sight, with complete anonymity, which makes it a logical centre for criminals to gather, discuss illegal activity, and sell illegal goods and services. Because of this, those bodies and security teams which are able to effectively monitor the blogs, forums, and chat rooms of the dark web have an invaluable source of information on nefarious or illegal activities - and are often among the very first to know about important and relevant information that may impact their company or organisation.  

Advanced warning for things like data breaches, reputational risks and physical threats to assets allow companies to effectively form strategies to deal with and mitigate the threats to their organisations.

These conversations and activities are highly relevant to many Signal subscribers, hence the addition of the Dark Web as a data source for Signal Gold subscribers in 2017.

Read our articles:

• Why a Dark Web Scan is Essential for your Business

• Mitigating the Threat of Data Breaches and the Risk of Credential Stuffing

Examples of activities that have been identified from dark web content include:

• Online markets selling stolen and fake goods

• Impersonation of individuals or organizations

• Details in regard to hacking or incitement to hack

• Reputational risk via fake news or impersonation

• Illegal activities such as drugs and drug paraphernalia

One of the benefits that Signal provides is the ability to review the dark web post content without needing to utilize a Tor browser – simply review the content from within your Signal browser session.

Dark web monitoring is available for Signal subscribers with a gold or better subscription– if you are interested in more information in regard Signal or the dark web content, then contact us info@signalpublicsafety.com

The Power of Emotional Analysis - introducing Signal Spotlight

Signal Spotlight provides a real-time overview of the emotional state of Signal search results. Using Signal Spotlight, Signal users can better understand the prevalence and drivers of emotions and what is happening in real-time.

Spotlight taps into the results from Signal search criteria across many data sources to better understand the prevalence and drivers of emotions. For example, during an incident or event, an important attribute is how people are feeling about what has happened and how the emotional state may be changing real-time as that incident/event unfolds.

The Spotlight underlying technology uses a large vocabulary of emotion terms that were compiled from multiple sources, including the ANEW and LIWC corpora, and a list of moods from LiveJournal.  In addition, a crowdsourcing task was run to organise these terms against Parrott's hierarchy of emotions. The emotions are colour-coded using a dataset of affective norms provided by the Center for Reading Research at Ghent University.

Spotlight leverages technology and research undertaken by the Language and Social Computing team in the Digital Economy Programme of CSIRO's Digital Productivity Flagship and originally developed as a joint project between computer scientists at CSIRO and mental health researchers at The Black Dog Institute.

Reference

Milne, D., Paris, C., Christensen, H., Batterham, P. and O'Dea, B. (2015) We Feel: Taking the emotional pulse of the world. In the Proceedings of the 19th Triennial Congress of the International Ergonomics Association (IEA 2015), Melbourne, Victoria, Australia, August 2015.