The first step to making informed security decisions is to develop accurate knowledge of the current cyber capabilities and the potential risks involved with the existing business processes. Without a clear picture of the organization's current capabilities, security leaders will not be able to accurately project the potential risks and vulnerabilities of the existing security infrastructure.Organizations must pursue the assessment of current security tools, policies, processes, human-cyber capabilities, and any third parties involved. In addition, security leaders must also assess any potential vulnerability or risks associated with current processes.
Cyber security is a very strategically driven sector - more like a game of chess, with businesses at one end and threat actors at the other. Whichever party has the most knowledge and predictive power, has the advantage. In most scenarios, cybercriminals are always one step ahead, which is why we are seeing an influx of successful breaches and sophisticated attacks. To take this advantage away from the threat actors, businesses need to achieve a totalistic and contextual view of their cyber risk posture.
Taking a knowledge-based approach allows organizations to switch from detecting to predicting cyber threats. With profound assessment and data-driven intelligence, businesses can start predicting potential threats and take proactive actions well before a threat arrives at the network gateway. Thus, security teams can potentially cut-off attack paths even before they are crafted by cybercriminals. By constantly measuring the projected threat level, security teams can start patching vulnerabilities in real-time, and always stay a step ahead of the threat actors.
A knowledge-based approach also creates a sustainable value for organizations. Blindly investing in security resources and solutions will evidently increase the financial burden for business leaders. Key decisions like security investments, strategy and policy implementation should be driven by profound data and intelligence. Informed decisions based on quantitative data will allow business leaders to invest in solutions and strategies that have long-term value.
Cyber risk quantification platforms generate risk and breach-likelihood scores based on the assessment of the organization’s security posture, using data science-backed risk engines that can feed information-driven confidence to security teams. The model aggregates signals across an organization's workforce, human-cyber capabilities, policies and processes, technology, cybersecurity products, and associated third parties to generate a quantitative measure for the entire security infrastructure.
Security teams can use this quantitative measure or risk score to identify the weakest links and security gaps across the entire infrastructure in real-time. The cyber risk quantification approach not only allows organizations to predict potential risks in advance, but also allows leaders to allocate resources efficiently across different areas of the business. Stakeholders are able to quantify the efficiency of their current security measures, products in use, and return on investment.
Furthermore, the risk quantification approach can allow organizations to communicate cyber risk to all relevant stakeholders. There are often scenarios where departments beyond the IT and security teams do not truly understand the cost of a security breach. Cyber risk quantification can generate a measurable metric to represent the likelihood of a breach and its financial impact on the entire business, in turn taking the guesswork out of cybersecurity. So, every stakeholder across the organization becomes aware of the potential threat and risk associated with a security breach.
New-age cybercriminals are becoming more aggressive and agile in their approach - whether it's by using advanced illicit technology, tailoring attacks with new sophisticated methods, or by automating attack paths in ways we have not seen before. Therefore, the only means of defending against this new age of threat actors is to change our security mindset and move beyond the traditional reactive approach. Cyber risk quantification allows organizations to effectively make the transition to a proactive security mindset, and start identifying and addressing risks before they lead to a potential breach or disruption.
The role of AI for cyber security is to help organisations reduce the risk of breaches and improve their overall security posture. AI works in cyber security by learning from past data to identify patterns and trends. This information is then used to make predictions about future attacks. AI powered systems can also be configured to automatically respond to threats and fight cyber threats in quicker timescales.
As the corporate attack surface continues to develop and evolve, analysing and enhancing cyber threats and cyber attacks is no longer a human-scale challenge. Depending on the size of your organisation, up to several hundred billion time-varying signals must be processed to calculate risk appropriately.In response to this unprecedented challenge, AI tools and methods such as neural networks have evolved to assist information security teams in protecting sensitive information, reducing breach risk and improving their security posture with more effective and efficient threat detection and threat removal features.
Machine learning is a subset of AI that uses algorithms to automatically learn and improve from experience without being explicitly programmed.
It is mainly used in cyber security for two purposes:
Anomaly detection: Machine learning can be used to automatically detect anomalies, such as unusual user behaviour or unexpected network activity, that could indicate a security threat. For example, products such as crowdstrike, darktrace and many others are using this.
Classification: Machine learning can be used to automatically classify data, such as emails or files, into categories (such as spam or malware) so that they can be dealt with more efficiently.
AI systems are also susceptible to being fooled by so-called “adversarial examples” – inputs that have been specifically designed to trick the system into making an incorrect classification. For example, an image of a stop sign that has been slightly altered so that it is no longer recognisable as a stop sign could fool an autonomous car into thinking it is something else, such as a yield sign. This could potentially lead to disastrous consequences.
As AI becomes more widely used in cybersecurity, it is important to consider the potential risks and how they can be mitigated. One way to do this is by ensuring that AI systems are “explainable” – that is, they can provide a justification for their decisions. This will help to ensure that decision-making is transparent and accountable and will help to prevent adversarial examples from being used to trick the system.
There are several good use cases for AI in cybersecurity. Starting with the researchers or think-tanks, here’s a good example of Gartner’s use-case prism for cybersecurity. Hyperautomation is a much-discussed topic since Gartner’s prediction – this means that another magnitude of automation will kick in on top of generic next-gen AI systems. This involves incorporating AI/ML along with automation + quality assurance to ease the managing of alerting and incident response work. In essence, it will help businesses augment no-code or low-code security at scale and improve business agility and DevOps strategies.
Transaction fraud detection
File-based malware detection
Process behaviour analysis
Abnormal system behaviour detection
Web, domain & reputation assessments
Asset inventory & dependency mapping optimization
Account takeover identification
Adaptive runtime access & entitlement
Identify proofing
Machine vs human differentiation
Text-based malicious intent detection
Same person identification
Web content visual analysis
Security operation task automation
Business data risk classification
Policy recommendation engine
Event correlation
Hazard intelligence
Security posture & risk scoring
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