There remains a significant disparity in utilisation of resources between defensive and offensive cybersecurity technologies. When comparing the return on investment (ROI) for defensive and offensive investments, security experts discovered that offensive security routinely outperforms defensive security. For example, penetration testing not only identifies vulnerabilities, but it also addresses and seals potential access sites for hackers.
This recognition should drive organisations and their security leaders to consider why there is so little investment in offensive security solutions. Many CISOs recognise a clear market gap in offensive security tactics, with acquired tooling fatigue unable to satisfy the changing needs of modern enterprises.
CISOs must now look into how a data-driven approach may generate a proven ROI for each offensive security expenditure they make.
Data science and cybersecurity: A powerful duo
In an era of digital transformation and networked systems, cybersecurity incidents have increased tremendously. Businesses face a slew of dangers, including unauthorised access and malware attacks. To tackle this, data science may give analytics that assist security leaders in making informed decisions about their cyber resiliency plans and tactics.
Data analytics, whether powered by security providers and in-house technology like AI/ML or threat intelligence feeds, entails identifying patterns and insights from cybersecurity data, generating data-driven models, and developing intelligent security systems. By analysing relevant data sources from security testing across assets, systems, customers, and industries (including network activity, database logs, application behaviour, and user interactions), they may deliver actionable intelligence to secure their assets.
However, the most significant component of data analytics is that it improves data-driven decision-making by giving much-needed context and proof behind user behaviours, whether authorised or unauthorised.
Data-Driven Decision Making in Offensive Security
Data-driven decision-making is the foundation for effective offensive security. Here's how it takes place.
• Threat Intelligence: Data analytics allows organisations to gather, process, and analyse threat intelligence. Defenders obtain real-time insights from monitoring indicators of compromise (IoCs), attack patterns, and vulnerabilities. These findings inform proactive steps like fixing key vulnerabilities and modifying security rules.
• Behavioural analytics: Understanding user behaviour is critical. Data-driven models detect anomalies and highlight questionable activities. For example, unexpected spikes in data exfiltration or atypical login patterns will prompt an alarm. Behavioural analytics can also help uncover insider threats, which are becoming increasingly prevalent.
Challenges and future directions
While data analytics can boost offensive security and decision-making, major challenges persist. Data quality is critical for accurate and actionable intelligence; as the phrase goes, "Garbage in, garbage out." Balancing privacy and ethics can also be difficult, but because security testing data should be free of PII, this should not be the primary focus, but rather intelligence that can help make better decisions.
Ultimately, offensive security practitioners must anticipate adversary attacks. However, the future seems promising, as data analytics can propel offensive security as a viable and evidence-based strategy. With analytics, security executives can proactively defend against attacks. As threats develop, so should our data-driven defences.
