The increased adoption of security AI in enterprises has yielded significant benefits in terms of breach detection and containment. In fact, according to IBM’s 2022 Cost of a Data Breach Report, organisations deploying security AI and automation incur an average of $3.05 million less in breach costs, making it the most significant cost saver. However, the efficacy of security AI depends on the right architecture.
A data breach is an expensive matter. Per IBM, the average costs of breaches by type are:
- $4.35M – The average total cost of a data breach
- $4.82M – The average cost of a critical infrastructure data breach. Critical infrastructure includes financial services, industrial technology, energy, transportation, communication, healthcare, education and public sector industries
- $5.57M – The average cost of a breach for organisations with high levels of compliance failures
By detecting and mitigating data breaches and compliance gaps in advance, organisations can preempt the damage they will potentially cost. Imagine how many activities you could run or how many people you could hire with those budgets.
Deployed within a big data architecture, security AI offers significant potential to enhance security by increasing detection and response speed, scalability, and intelligence.
One area where AI is particularly vital to security is application programming interfaces (APIs). As APIs transfer sensitive data, they are often targeted by cybercriminals. Detecting API attacks is particularly challenging because they involve finding bugs in application logic, which can be difficult to identify during development. AI is uniquely suited to detecting behavioural anomalies in millions of API calls and correlating them over time to detect malicious activity. Existing security mechanisms like WAFs and API gateways lack the context to detect such interconnected activities.
AI offers benefits in terms of scalability, particularly as the amount of data to protect has grown exponentially, making security increasingly complex. AI algorithms can analyse massive amounts of data in near-real-time, enabling organisations to detect attacks quickly. Additionally, attackers are increasingly using automated AI to enhance their attacks, which means that defenders need to leverage AI security capabilities to counter these new threats.
AI algorithms require time and attention to become smarter and leverage crowdsourced experiences. Security AI algorithms need to run in thousands of customer environments for years to gain the necessary learning for accurate detection. Next-generation AI security solutions, like firetail.io, provide organisations with an unprecedented context of deep intelligence to identify malicious activity and uncover vulnerabilities before they can be exploited.
In summary, security AI transforms security in three ways: it increases detection and response speed, scalability, and intelligence. However, organisations must deploy security AI within a big data architecture to maximise its potential. Moreover, AI algorithms require years of experience to provide effective results, and next-generation.