There’s a common misbelief that simply procuring a Runtime Application Self-Protection (RASP) solution and implementing it in any manner will provide the same level of assurance for mobile applications. However, the reality is far more nuanced. The level of protection offered by RASP depends heavily on the implementation method, its integration with the app, and the threats it is designed to address. To truly leverage the potential of RASP, it’s crucial to understand how different approaches offer varying levels of assurance and protection. In this blog, we explore these implementation methods, their strengths, limitations, and how they cater to the unique needs of mobile apps.

The modern digital landscape is a battlefield, where cyber threats constantly evolve and escalate. Organizations are under immense pressure to demonstrate tangible value from their security investments. This has led to a growing emphasis on key performance indicators (KPIs) like detection fidelity (the accuracy of identifying true threats), asset coverage (understanding the full scope of what needs protection), Mean Time to Detect (MTTD – how quickly threats are found),Mean Time to Respond (MTTR – the speed of reaction), and Time to Mitigate (TTM – how long it takes to neutralize a threat). Generative AI (GenAI) is not just enhancing these KPIs; it's fundamentally transforming how organizations manage vulnerabilities and exposures.

The common belief that limiting app installations to non-rooted Android devices ensures comprehensive security is a significant oversight in enterprise mobile security strategy. Although non-rooted devices come with inherent security features, they remain prone to sophisticated cyber threats, particularly from malware-infected or malicious applications that might not require root access to perform damaging activities.

Shadowserver, a globally recognized cybersecurity watchdog, liaises with security organizations, national governments, and CSIRTs to dismantle global cybercrime networks. They achieve this by collecting and analyzing data on malicious internet activity from scanning the IPv4 internet over 100 times per day and utilizing a vast network of honeypots, honeyclients, and sinkholes worldwide. Additionally, Shadowserver collaborates with governments, industry partners, and law enforcement agencies to collect and analyze malware and botnet data.

APIs come in various forms, each with unique security challenges. Public APIs are exposed to the internet and often poorly secured, making them prime targets for attackers. Internal APIs are used within an organization but can be accessed by insiders or through compromised systems, posing internal threat risks. Third-Party APIs integrate with external services, which might have varying security postures, leading to potential security gaps. Legacy APIs are older and might not have been updated to incorporate modern security practices, making them vulnerable to exploitation. Additionally, Unsecured Endpoints are APIs lacking proper authentication, authorization, or encryption, offering easy entry points for attackers.

The recent arXiv paper, "LLM Agents can Autonomously Hack Websites," reveals a groundbreaking development: Large Language Models (LLMs) like OpenAI's GPT-4 are now capable of autonomously exploiting web vulnerabilities. This advancement highlights the potential and risks of deploying these powerful AI models in the realm of cybersecurity.

The pace at which vulnerabilities are exploited is accelerating, presenting an ever-growing challenge to organizations safeguarding their operations. Attackers increasingly use automation and easily accessible exploit toolkits, demanding proactive cybersecurity strategies that prioritize real-time threat intelligence and active defense mechanisms. By adopting preemptive security measures, organizations can protect themselves against immediate threats and enhance their resilience against future vulnerabilities.

The rise of automated penetration testing solutions has been hailed as a game-changer, promising to revolutionize how organizations defend against cyber threats. However, a closer examination reveals a stark reality: many of these solutions are stuck in a loop, repeatedly focusing on a single use case—credential discovery and reuse—without pushing the boundaries to explore more advanced and varied attack vectors.

NST Assure Continuous Threat Exposure Management (CTEM) platform is a one-of-a-kind solution that enables security consolidation, or “Platformization,” within the external threat management space.

Are you still relying on vendor risk assessment methods focused solely on Self-Assessment Questionnaires (SAQs) and passive detection of the attack surface? Do you use the same standardized risk assessment processes for all your vendors, overlooking their distinct risk profiles and levels of system integration?

Remember the Facebook and Cambridge Analytica scandal? How about the time Strava's fitness app accidentally pinpointed secret military bases worldwide? These infamous cases, offer a stark lesson: excessive data exposure remains a serious security threat fueled by overly verbose APIs.

Attackers strategically exploit weaknesses in popular and widely used software products requiring internet connectivity especially those from vendors with known security flaws. These vulnerabilities, ranging from minor to critical, provide entry points for diverse attackers – from nation-states to organized cybercriminals – who use both broad as well as targeted approaches.