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.

5 Ways AI is Subtly Shaping the World as we Know it

Artificial Intelligence (AI) describes technologies that can make informed, non-random decisions algorithmically. It has many current and potential applications, it is the current pinnacle of humanities ceaseless drive towards greater and greater efficiency. In particular regard to OSINT though, it enables humans to collect, analyze and interpret huge sets of data, data sets so large that it would be entirely unfathomable to even approach them without machine assistance.

Everyone knows AI is shaping their world in one way or another. But often the changes are subtle, gradual and go unnoticed. Very few of us know what actually goes on behind the steel doors of the big tech companies like Alphabet, Facebook, and Apple. And yet we interact with their AI systems on a daily basis and those systems have huge power over our lives. In this article, we take a look at some of the key ways AI is being used today and how it will become increasingly important as our technologies improve.

5 Ways AI is Shaping the World 

1. Improving and optimising business processes 

The very first robots in the workplace were all about automating simple manual tasks. This is the age of factories and production lines. Today though, it’s not manual tasks that robots are taking over. Instead, software-based robots are taking on repetitive tasks carried out on computers. 

Initially, this was limited to automating simple repetitive tasks, such as “send follow up email 2 if no response after 3 days”. This has already reduced admin tasks and improved business operational efficiencies immeasurably. The next step though is the use of AI technologies to further alleviate some of the more labour intensive ‘intelligent’ tasks such as data gathering, aggregating and analysis, leaving people to spend more time on complex, strategic, creative and interpersonal tasks.

2. More personalization will take place in real-time

Big tech companies are already using data to personalization services. Google Discover, for example, is a feed based on a complex algorithm which reads your online history and tailors the news feed to your particular interests. Other big tech examples are Spotify and Netflix which use AI to suggest relevant media based on your historical behaviour. 

This technology is constantly being evolved and is probably one of the most noticeable in our day to day lives. The end goal is a system which can almost perfectly predict your desires and needs, an outcome none of us are likely to protest against. On the other side of the same coin though is the use of that very same data to target individuals with hyper-relevant ads. This practice can often seem intrusive and is one of the driving forces behind the adoption of VPN’s.

3. AI in the creative space

Some things are still, even in 2020, better handled by humans. That being said AI technologies are now beginning to encroach on the creative spaces. Scorsese's, The Irishman, is one example of this, where Robert De Niro was de-aged on-screen using AI technology. 

There are additional uses though, for example, AI is being used to edit video clips for the purposes of spreading misinformation, and often these edits are incredibly hard to spot. This has led to a new sector of cybersecurity which requires AI technology to spot AI-generated or edited video and audio files. 

4. Increasing AI in Cybersecurity

Even as data grows and is used to progress the development of AI this simultaneously opens up new avenues for exploits by threat actors. For example, AI can be used to create and automate targeted ‘intelligent’ phishing campaigns. AI-supported cyberattacks though have the potential to go much further.  As such, increasingly advanced AI is needed to combat the evolving cyber threat landscape.

Related: How Machine Learning is Changing Modern Security Intelligence 

5. AI learning to perfectly emulate humans

Anyone that keeps their eye on the work that Google is doing will know about their 2019 update, BERT. A natural language processing (NLP) framework which is designed to better understand context and intertextual reference so that they can correctly identify both the searcher's intent as well as the intent behind any content created. 

One of the key challenges that faces AI right now is idiomatic or referential speech; language that has more depth of meaning, for example, determining the importance of the concept of a mother, or understanding a phrase like “six feet under”. Our current research and development project at Signal is one example of the practical applications of overcoming this challenge. It involves using machine learning to enable our software to understand the intent behind text, even when ‘hidden’ behind challenging language like idioms, to more accurately identify threats.  

As these natural language processes advance, so too will conversational AI bots, to the point where, because of the range and complexities of their answers, you would be forgiven for mistaking them as human.

The Future of AI and what that means for OSINT

Artificial Intelligence, machine learning, and automation have already revolutionized intelligence gathering. With OSINT tools like Signal security teams and intelligence agents can effectively and efficiently monitor the open, deep, and dark web, setting up customized alerts based on searches that leverage boolean logic. Machine learning takes this intelligence to the next level. It allows for vast amounts of data to be collected, aggregated, and for all the irrelevant hits to be essentially culled, supplying the security team at the end with actionable, relevant intelligence.

Humans play an essential role in this new intelligence lifecycle. In defining the search terms to match security strategies, analysing the end date the system feeds back, reassessing the searches based on the new evidential data and implementing appropriate responses. This is a key role that will no doubt evolve as the technology becomes more accurate, reducing inefficiencies in process.