Data poisoning against security software that uses artificial intelligence (AI) and machine learning (ML) is likely the next big cybersecurity risk.

We live in a world in which threats are constantly growing and morphing. This empowers leaders to understand what is happening, where it's happening, and why it's happening -- so they can take decisive action to protect citizens. Advancements in artificial intelligence (AI) are driving unprecedented change and opportunity in this space. The ability to merge physical models with digital content and conduct analysis at extraordinary speed is a game-changer for the GEOINT workforce -- but such a major industry shakeup also presents new challenges. A new study by the United States Geospatial Intelligence Foundation (USGIF) and MeriTalk, "Mapping AI to the GEOINT Workforce," shows that 91% of geospatial intelligence stakeholders believe AI has the potential to greatly improve the discipline -- particularly in the areas of national security, emergency response, and urban planning and development.

IBM Watson may be able to win Jeopardy!, but security experts are skeptical about the technology's ability to defeat today's cyberthreats. The IBM Watson for Cyber Security beta program launched this week with 40 partners around the world in an effort to help security analysts make better, faster decisions from vast amounts of data, but experts said this is the same promise offered by many other products. IBM said Watson for Cyber Security will feature natural language processing that can help it to "understand the unique language of security." "The truth is, a lot of security vendors today are attaching [artificial intelligence] or cognitive to a number of products that are really just advanced analytics or machine learning, which are also important elements that can help in the fight against cybercrime," Diana Kelley, executive security adviser for IBM Security, told SearchSecurity. "What Watson will bring to the equation that is unique is the ability to digest vast amounts of both structured data, as well as all of the intelligence that exists in natural language, like blogs, white papers and research reports. For example, there are around 10,000 security research papers published each year, and 60,000 security blog posts published every month."

IBM Watson may be able to win "Jeopardy!" The IBM Watson for Cybersecurity beta program launched this week with 40 partners around the world in an effort to help security analysts make better, faster decisions from vast amounts of data, but experts say this is the same promise offered by many other products. IBM said Watson for Cybersecurity will feature natural language processing that can help it to "understand the unique language of security." "The truth is a lot of security vendors today are attaching '[artificial intelligence]' or'cognitive' to a number of products that are really just advanced analytics or machine learning, which are also important elements that can help in the fight against cybercrime," Diana Kelley, executive security advisor for IBM Security, told SearchSecurity. "What Watson will bring to the equation that is unique is the ability to digest vast amounts of both structured data as well as all of the intelligence that exists in natural language, like blogs, white papers and research reports. For example, there are around 10,000 security research papers published each year, and 60,000 security blog posts published every month."

Consistently predicting the next disruptive company is the holy grail if you are interested in start-ups. We've been testing out some Deep Learning techniques on our data to help make such predictions, and have had some interesting early results we thought we'd share. Word2vec is a Deep Learning technique first described by Tomas Mikolov and his team at Google in 2013, and in basic terms it allows a model to be built for a particular dataset (or corpus) in which words are represented as vectors. One of the most interesting outcomes of this approach is that we can gain insights about text by analysing word vectors arithmetically. In a classic example of the power of Word2Vec (trained on a large dataset), the vector of Queen is found to be almost equal to King Woman – Man.