A dive into Cybersecurity Asset Management
With the onset of COVID-19, many organizations have transitioned to remote-only operating models, which has
increased their vulnerability to cyber threats. Malicious cybercriminals are exploiting this situation through phishing
attacks and disinformation campaigns. They target video conferencing services to gain access to employees’
credentials, which are then sold to other cybercriminals. Additionally, cybersecurity risks encompass vulnerabilities,
misconfigurations, and excessive privileges—whether in on-premises systems, across cloud environments, in OT
(Operational Technology) environments, or web applications. Consequently, a robust security solution with an
excellent detection rate has become essential.
Companies need to adopt comprehensive cybersecurity asset management to gain full visibility into all assets and
cybersecurity risks, regardless of the data source (such as vulnerability management, web application security, cloud
security, active directory security, etc.). This centralized view streamlines analysis, simplifies reporting, and helps
organizations take action more swiftly.
The key steps include:
- Gain Complete Visibility: Obtain comprehensive visibility across all assets and their contexts, including
devices, users, networks, applications, and data. - Compare and Manage Risks: Evaluate and manage cybersecurity risks between business units or
locations internally and against industry peers externally to determine where and when to invest human and
financial resources. - Unified Security Workflows: Understand the impact of security incidents, identify security and compliance
gaps, and address them effectively. - Identify and Remediate: Use workflows to send high-priority remediation tickets to developers, track
progress, receive updates, and verify completion.
Incorporating Generative AI:
Harness the power of generative AI for preventive security. Generative AI can integrate asset and risk context,
playing a crucial role in guiding decisions related to attack surface and exposure management. It accelerates
cybersecurity risk assessment, provides valuable insights into cyber asset management, and offers instant access to
exposure management remediation guidance.
Although the recent surge of generative AI brings with it many benefits and assets, there are also inherent risks. AI
systems face several risks, including the generation of misleading or false information, vulnerability to prompt
injection attacks, and potential data breaches that compromise privacy. They may also perpetuate biases, be
susceptible to adversarial manipulation, and produce inaccurate results due to drift or hallucination. Legal and ethical
violations are possible, and inadequate testing can lead to unreliable deployments. Additionally, AI systems can be
overloaded by denial of service attacks, and lack of transparency can make it difficult to understand how conclusions
are reached.
The solutions is simple:
To ensure safe and effective AI use, establish a governing body to set AI guardrails and best practices, conduct
thorough risk assessments aligned with specific management priorities, and implement robust data and security
measures. Focus on securing critical functions where AI can add value while managing associated risks. Additionally,
develop continuous evaluation benchmarks and enhance security measures to maintain ongoing protection.
