Building a safe future for business’ critical data requires a collaborative effort between machine learning capabilities and traditional human decision-making.
In a climate where IoT and cloud computing are pushing global enterprises’ data flows into vaster and faster environments than we’ve ever seen before, security teams need to find ways to concentrate their efforts on protecting the data that matters most.
Using the concerted efforts of man and machine, security teams are exploring ways to cut through the noise of these increasingly busy environments. This is a problem that is only going to become more challenging as technology moves towards the cloud and networks require more context, more logs and a more flexible environment. We’re facing a future that will be highly ‘consumerised’: the world outside business is going to become one with the world inside business, and our personal and work identities will merge. AI will be essential to providing security that can respond almost instantaneously to the flex and flow between our different use situations.
By training both analysts and machines to define and visualise what normal network activity looks like, businesses stand the best chance of knowing when something isn’t right and taking fast, decisive action.
AI’s capacity to spot near-invisible anomalies amongst a mass of ‘normal’ data continues to grow. This puts the onus on the security industry to do more than keep pace by finding ways to mitigate insider and outsider threats; the true challenge is to turn AI’s detection capabilities into the ability to remediate.
Man and machine: finding the balance
The majority of our networks and systems have sophisticated automation capabilities to deal with high volume, low sophistication attacks. But developing AI beyond automation is about accelerating what the analyst can do in the event of a more complex threat.
Businesses should focus on using an intelligence augmentation (IA) strategy: the practice of using machine learning and automation to complement the intelligence of the human, so they’re free to make meaningful contributions at a higher, more sophisticated level.
Security teams are facing an average of 174,000 alerts per week, and are only able to review around 12,000 of them, with approximately 4 days to resolution. When you consider the fact that today’s average global business’ broad data logs show roughly ten million events per second, it’s no surprise that analysts are struggling to keep pace.
Couple the sheer volume of data with the subtle and, often unknown, nature of the patterns that arise within these logs, and you have a mass of ‘noise’ that humans just can’t cut through by themselves. Analysts aren’t just looking for known threats, but also the threats they haven’t seen before. How can they be expected to spot an anomaly without any preconception of its form?
Enter AI. For security teams to stand the best chance of preventing threats, they need technology capable of focusing their search parameters by condensing billions of daily data points into hundreds. More than this, they need these data points, from across several data sets, classified and correlated into meaningful patterns and significant events to be presented to the user in a visual, accessible form.
Using the time and energy saved by automating this initial process, the analyst can really excel by focusing on events of interest and making meaningful contributions to the defensive process. They can study the unusual patterns and anomalies flagged by AI and determine their value and importance. Do we investigate this further? Is it a threat? How do we mitigate it and how will the action we take affect our live business operations?
Once an analyst answers these questions and determines the threat level — if any — they can feed this new information back into the AI-enabled machine; a process that enables the machine to learn alongside the analyst. This way similar threats are picked up faster and with a more specific classification.
The loop between analyst and AI is closed — the human and the machine work together to learn, develop and improve our cyber defences for the future.
A holistic view of security: the current state of play
Interactive visualisation is one way in which security teams can work towards incorporating this holistic loop into their security strategy. It’s an effective means of increasing the bandwidth between human and machine by mutual interaction; the machine is able to suggest its findings, whilst the human can explore these suggestions, interpret, validate and feedback new knowledge for the machine to learn from.
Nexus, an AI-driven tool powered by artificial neural networks, developed at BT can be used as one of the first warning signs for analysts facing a potential attack.
Using graph analytics, Nexus consults its learned perception of a network’s normal environment to flag up anomalies and plot them on an interactive graph — contextualising their behaviour by allowing direct comparisons to the ‘normal’ clusters of data on the same graph. Not only is this useful in the initial stages of an attack, but analysts can also use the visual data to discover and study behavioural patterns of advanced persistent threats (APTs): intruders that have remained undetected for an extended period of time.
In the case of initial attack discovery, if an analyst decides the anomaly Nexus has flagged may pose a real threat to their business’ security, they can turn to another of BT’s self-developed tools, Saturn. This is a powerful visual analytics environment that allows the user to visualise several diverse forms of data grouping. The security team can pull up the geographical positioning of the potential breach, pinpoint its exact locations across the network and study its behaviour within specific time parameters.
It’s critical that the analyst is presented with these contextual and plotted representations of data. In cutting out the early, time consuming stages of data analysis, security teams can focus their brainpower on answering the really valuable questions: why is this device acting strangely? How does it compare with other similar devices in the environment? How might it be impacting critical data stores?
With the AI-based cyber security market expected to rise from £9Bn to £26Bn by 2025 , we can expect to see a whole new level of sophistication in mixed initiative analytical tools, but our own Saturn and Nexus are some of the most promising options currently in deployment.
The dark side of transformation
As investment rises in AI for businesses’ cyber defences, we should be wary of our adversaries progressing in the same vein. An increase in AI-driven attacks will require the need to fight fire with fire. This means working on AI that will hunt down AI-driven threats and deploy automated responses to mitigate them.
Just as we’re seeing deep learning capabilities being used to help understand large volumes of network behaviour; attackers are regularly launching large-scale phishing campaigns. Rather than a group of attackers hand-crafting emails impersonating a friend or family member, AI can be used to mine individuals’ or business’ private data online.
We could see masses of emails being delivered — all tailored to a specific reader, and all completely automated. As attacks grow even more sophisticated, security teams are encountering advanced botnets and AI-enabled malware similar to those we’ve already witnessed in the media — intelligent threats that learns to avoid detection within a victim’s IT estate.
Although there’s certainly cause for businesses to prepare for this level of criminal-sophistication, it’s important to remember that threat actors are subject to the same commercial challenges as businesses — their effort must match the reward.
Deploying AI technologies at this level still brings a mixture of reward and risk for investors on both sides of the fence; although the potential is huge, AI systems can increase the attack surface and produce new and unforeseen vulnerabilities.
Looking to the future
The future of cyber defence is set to move beyond using AI as a single point implementation solution. Instead, it will be used across a much wider context, relating a change or interaction to the bigger picture and triggering the right level of security, adapting naturally as environments shift. When anomalous behaviour by an endpoint triggers an alert, AI will respond by taking that information into account across all other systems. For example, when an individual gets a new PC that needs patching or security applying to it, AI will take this into account by temporarily blocking the ability to download corporate files.
AI will not only cut out the ‘noise’ created by vast volumes of data by identifying anomalous events, it will learn to apply logic to a situation to further remove human involvement. When faced by a field change in a data log, an AI parser won’t stop working — it’ll make a jump in its logic and continue. AI will apply the known policies to the intended action and make contextualised guesses to help keep the systems flowing. Where once human input would have been needed to write new code to deal with a situation that doesn’t meet standard criteria, AI will manage by using a set of pre-learnt use actions and becoming self-repairing. This will be essential if security is to keep pace with a fully IoT-enabled world, where unknown devices will be connecting and interacting with the network much more quickly than today.
As we move towards a zero-trust environment, and the use of technology grows exponentially, it’s AI that will match the security deployed to the user, rather than to the network or device. AI will become business as usual in the background, applying security automatically in response to user activity — potentially merging personal and business security.
The challenge for AI in security is to identify which situations need a context-rich decision (and therefore human input), and which can be handled by AI alone. AI can’t stand still: as our digital environments grow and mature, its ability to contribute to security needs to keep pace. The more we learn, the more we can push AI to learn.
Pushing the boundaries of innovation
As a global network operator, our security teams are in a unique market position to commit to the improvement of AI technologies. Unlike a security defender that focuses solely on developing malware products, we process over 2.1 billion events an hour, analysing traffic and data flows on a global scale.
Combine this with our partnerships and understanding of other global data platforms, and you get multi-technology, multi-environment conversations that come close to building a full picture of what’s out there, and how it can be protected.
Our 3,000 dedicated security professionals and 15 security operation centres worldwide are working hard to progress the future of cyber defence. As well as our powerful visual analytics tools, we’re developing virtual reality and digital twin technologies to simulate safe training environments for the next generation of analysts and to enable a rich environment for research into behavioural patterns of data.
On top of our leading position in AI training, our research team are working towards incorporating blockchain technology and quantum computing into our defensive strategy, as well as building automated, self-healing defence systems akin to the human immune system. These systems will rely on a combination of discovery and response; learning from each discovery and building automated responses based on previous success rates.
Our security research team filed upwards of 30 patent applications in 2018, covering areas such as device vulnerability and behavioural change detection, blockchain-based privacy, and IoT security. With this level of investment and innovation, our development of unique and powerful cyber defence technology promises to grow into the future.
The future will never be fully automated
Although the capabilities of AI are huge and yet to be fully realised, the value of the human will never truly be rivalled.
The challenge remains in being able to quantify uncertainty with a prediction. We must make sure that users of AI tools are aware that they will never be 100 per cent accurate; they are indicators and aids but rarely definitive answers.
We must strive to automate our security processes wherever we can, but there will always be a collaboration between people and technology. The human is the innovator and the decision maker, integral to deciphering cyber threats. AI systems provide high volume, high quality data analysis — an equally necessary perspective. Only by creating technology that maximises the bandwidth between the two, can we hope to build a powerful team for the future.
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