Defense & National Security
Use Cases
Newsfor Defense & National Security
Accelerate product discovery based on modelling of components
Accelerate product discovery based on modelling of components - potentially millions of input options can be modelled to ascertain the most promising.
Automate aircraft piloting
Autopilots are transiting to AI-powered devices. This will potentially shift the boundaries of what will be possible - and in time possibly customer expectations and willingness to accept risk (e.g. airplanes without pilots).
Automate code breaking
Espionage related cracking of opponents secure communications. Techniques used by many actors - but states still retain critical edge.
Automate cybersecurity systems
Utilise learning systems to effectivel and swiftly respond to security threats, many of which may have been delivered with machine learning support. This is a game where every innovation in defence triggers the next innovation in attack - and vice versa.
Automate operation of military installations e.g. sentry guns
Deployed military installations will increasingly use AI for autonomous decision-making - often with a lethal impact. Examples include sentry guns (deployed on the Korean border) and so-called ''intelligent mines''.
Censor user generated content on social media and other platforms
Censor user generated content on social media and other platforms. or This may be used to reduce hate speech or incitements to violence similar negative issues - but can also be used for political censorship. The line between the two can become a matter of debate.
Crack security systems
Breaking security systems is a growing field of study by a variety of actors - often of the malignant sort. Whether predicting PIN numbers from acoustic signals or spoofing iris images this is a race between attack and defence.
Detect fake biometric credentials
The rise of biometric-based security systems poses risks that fake credentials - photographs, scans, copies or even post-mortem presentation - may be offered to fool security systems. Image recognition is used to scan for and observe minute differences that may suggest the credentials are invalid.
Detect manipulated or falsified media
Detect manipulated or falsified media to discover attempts to damage reputations or create unwarranted legal situations. The rise in concerns over `fake news` deployment - especially at such sensitive times as during an election - is a growing area of security and media concern. with potentially huge ramifications.
Detect suspicious nautical vessel activity indicating overfishing or smuggling
Using image and location tracking data to identify suspicious behaviour patterns by nautical vessels that might indicate, for example, over-fishing. This would also include multi-vessel activity to predict load transfer. Similar technological approaches might be used to identify other nautical malfeasance such as smuggling.
Identify concealed weapons with radar
Radar is used to scan individuals to identify shape and metallic content of devices being carried, including those concealed under clothing. Machine learning is then applied to the data to identify potential weapons - be they guns, knives or even bombs. Inevitably there is, at this stage, a high risk of false positives that will potentially impact on human responses to triggered alerts.
Identify illegal or inappropriate images on devices confiscated by the police
Gathering evidence for criminal prosecution increasingly requries accessing suspect''s phones or computers to seek out potential clues - including from photos. In the era of the selfie and cheap computer memory this can potentially involve thousands of images. Using machine learning to identify weapons, drugs or nudity in stored images can considerably speed up the process - although false positives remain an issue.
Identify illicit use of natural resources, including land and forest
Monitoring and identifying the illicit use of natural resources for activities such as illegal farming, logging, deforestation, etc.
Identify individual''s activity likely to result in accidents
Typically using video or camera technology to spot individuals whose activity is at risk of causing accidents or other problems. Relevant images will be flagged for potential further intervention.
Identify items of concern in the mail
Volumes of mail are such that it is almost impossible to consistently check for items of concern - whether smuggled drugs or packages of suspicious substances likes anthrax. Using X-rays or similar sensor technology large volumes of images can be processed to indicate which items of mail should be properly investigated.
Identify people through walls
Using wifi signals to identify which individuals are moving - even through physical barriers such as walls. Potential real world uses will include security or facilities tasks - and in the medium term various healthcare or interactive gaming scenarios.
Identify persons of interest to law enforcement through facial recognition
Identification of "persons of interest" for law enforcement to intercept or track through facial recognition is a high profile use case for AI. Inevitably the debate about the trade-off between individual liberty and security effectiveness plays out differently according to local political expectations. In addition, actual levels of accuracy are a concern both in terms of effective implementation but also civil liberties (algorithmic bias is especially concerning on different ethnic faces for example). In some countries such as China emphasising the supposed effectiveness of the algorithms is a key part of national security communications strategy.
Identify security-related individual targets from range of data including sensors, camera feeds and suspect activity
Identify security-related targets from analysis of sensor feeds (e.g. drone cameras) typically matched with image recognition technology. Increasing debate about the extent to which this is tied to lethal capability and the role of a trained human in the decision loop.
Identify social media users through cross platform facial recognition to deploy phishing or marketing
A key part of hacking or similar scams such as phishing is building a larger database of potential targets and a better understanding of their profiles. One AI technique to support this is to use facial recognition across platforms to identify multiple accounts. Depending on regulatory data rights similar techniques can be used for more conventional marketing.
Integrate security systems at public entertainment venues such as sports stadiums
Managing event security has the benefit of operating inside a closed environment where huge volumes of data can be generated by multiple video, sensor and other data feeds. Integrating this and ensuring fast and reliable data visualisation and risk monitoring is a sophisticated AI use case.
Manage cybersecurity threats and regulatory reporting
Determine whether there is attempted and likely cyber breaches that could impact sensitive data and company operations. Also follow compliance reporting regulations for breaches.
Mimic creature or animal behaviour with drones or robotics
Mimicing animal behaviour in drones or robots has several potential uses: camouflage (for example in a security situtation), research or media support (allowing humans to better interact with creatures in their natural environment) or replicating (or even replacing) tasks currently performed by natural creatures (pollination for example).
Mininise need for sensors through generating likely input data from other sources
Sensors can be expensive, hard to maintain or simply unavailable for what can be important data. Using data from other sources can enable the predictive modelling of other data sets. Note that this can create a series of new risks, especially if historic patterns break down or feedback effects occur.
Monitor potential threats across battle space
Using satellite, drone and other data feeds to monitor potential risk factors on a macro scale across a potential battlespace. This might be a country - North Korea - and it might involve specific issues - in this case deployment of nuclear missiles. The potential scale, and opportunities for spoofing and mistakes, is huge.
Monitor transport fleet networks in real time to ensure safety of passengers and drivers
The roll out of peer to peer transport apps creates potential security risks, particularly in markets where personal checks on users and drivers are less developed. Automated systems will highlight beahvioural anomalies that may indicate a developing security or health situation and trigger both back-up enquiries and potentially further action.