Cybersecurity is one of the biggest challenges for companies in the digital age. It is estimated that a midsized organization receives an average of 200,000 cyber alerts every day, a massive volume that a typical team of security experts within any organization cannot handle. Therefore, artificial intelligence (AI) enabled cybersecurity is becoming increasingly necessary. AI technologies significantly improve and promote new products, services, and business models in a way previously not possible before. AI endeavors to simulate human intelligence, and a well-trained AI system generates alerts of threats, identifies new types of malware, and protects organizations’ sensitive data.
There is a vast spectrum of smart technologies that enable effective cybersecurity – including robotic process automation (RPA), cognitive automation, and artificial intelligence. Cognitive automation and AI are rapidly evolving with the capabilities of augmenting/mimicking quantitative human judgment with machine learning and NLP, augmenting human intelligence with predictive decisions, and mimicking human intelligence with machine intelligence. This swift evolution can be attributed to a few factors like:
– Machine learning advancements are improving the ability to identify predictive risks to identify emerging risks accurately.
– Financially it is feasible and makes sense to operationalize predictive risk models as automation and computing capabilities get faster and cheaper.
– The availability of rich internal and external datasets.
– AI’s growing capabilities like generating its own hypothesis and providing recommendations to address them.
– Use of risk management to drive business value by gaining predictive insights.
Implementing AI in cybersecurity
With a considerable surge in unstructured data from various sources, data collection and AI algorithms are becoming the cornerstones of the cybersecurity industry. Automated decision-making and evaluation processes can provide a wide range of protection from malicious and suspicious activities. Hence, implementing AI to protect data is the priority, and a proper roadmap to achieve this needs to be formulated.
– Identifying data sources. Deciding upon whether to buy or build data platforms to operationalize AI.
– Selecting the proper use cases with higher benefits and lower implementation complexities.
– Improving the logic of AI algorithms for effective threat detection by collaborating with crowd-sources (external) platforms.
– Enable rapid response to the detected threats by deploying security, orchestration, automation, and response (SOAR) and improve security management.
– Training a cyber team to ensure that an AI algorithm identifies and closes any potential threats entry points.
– For long-term improvement, governance for AI is essential.
Benefits of AI-powered cybersecurity systems
AI technologies and analytics can take an organization’s cyber capabilities to the next level. A few benefits include:
Automated and early threat detection – Early detection of threats bring down the various operational and hidden costs. AI has proven to be one of the best technologies in mapping and stopping threats. AI systems can potentially be trained to detect early a dedicated denial of service (DDoS) attack.
Optimized asset management – With a rapid increase in the number and use of the Internet of Things (IoT), it opens up the attack surface. AI helps manage the burgeoning of these connected devices, navigating their firmware updates and security patches.
Self-learning – Advanced machine learning capabilities in cybersecurity enable systems to learn as they grow with each success and failure, self-tuning to become more effective and efficient.
Better vulnerability management – Vulnerability management is the key to securing an organization’s network and is becoming more difficult. AI helps analyze and assess existing security measures, identifies weak points, and helps organizations focus on critical security tasks.
Explainability – AI assists in bringing out the root causes of defensive failures, making it easier to improve both infrastructure and deployment.
In the list above of benefits, we deliberately excluded a vital benefit – breach risk prediction – where the AI systems learn to predict the risk of a breach in a wide range of situations. This is achieved with predictive AI, an unsupervised-learning AI which is context-aware. Predictive AI automatically collects, analyzes, and tests data, and is often seen in applications like anomaly detection, threat detection, and cybercrime prevention.
How does predictive AI strengthen network security?
- Predictive AI can monitor and detect threats in real-time, reducing massive amounts of data storage requirements. It can identify patterns and develop a human-like understanding within constantly changing conditions.
- These self-learning AI systems can detect even the slightest deviation from the expected baseline behavior and send alerts to a security team.
In this age where data is being collected, stored, and consumed at an unprecedented rate and the volume of data generated will only increase exponentially over time, cybersecurity is and will continue to be of high priority for organizations. Fortunately, advances in AI technology, including predictive AI, are also increasing exponentially, arming organizations with the best tools to protect valuable data.
Are you looking to implement AI to protect your data and prevent cyber-attacks? Send us your query to email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with . develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.