How can artificial intelligence be used to identify and mitigate risks?

Artificial Intelligence (AI) plays a pivotal role in aiding security teams in the assessment and mitigation of risks by meticulously analyzing diverse factors, including vulnerabilities, threats, assets, and controls. AI functions by aggregating data from numerous sources, such as network logs, threat intelligence, and user behavior, thereby constructing a holistic risk profile. Through the incorporation of machine learning and natural language processing, AI has the capability to discern patterns, trends, and anomalies, serving as a valuable tool in identifying potential risks. This enables AI to promptly flag suspicious activities and prioritize them based on their assessed impact and likelihood.

AI helps spot and tackle risks by predicting future trends, detecting anomalies, and learning from data. It automates routine tasks, analyzes user behavior, and assesses financial and supply chain risks. In areas like credit scoring, compliance, and healthcare, AI provides dynamic risk scores and quick adaptability to changing landscapes. Its role extends to environmental monitoring, identifying risks related to climate change and natural disasters. The key is integrating AI seamlessly to enhance risk management across different domains.

Establishing the baseline of what relevant ‘bad’ looks is critical to helping minimise false positives and improve performance. The risk assessment helps do this. Further, each action in a system is a signal or indicator that tells or shows you something, particularly when they occur in a particular order. Your risk assessment needs to be detailed enough to articulate what a malicious act could look like. This sets the guardrails for your detection activity. AI plays an increasingly important role in detecting new and emerging threats, or subtle changes in threat actor behaviour in response to changes in your control environment or detection systems, but in my experience deploying AI is more effective when focused on your risk exposure.

Most of AI inference techniques are expensive at this stage, SIEM and tools security solutions are good at correlation and performing data analytics at scale. Lightweight ML models are already assisting in IPS, AV, NDR technologies, however, LLMs should be used at high level to perform contextual analysis or risk assessment on the information generated by rule based engines.

A dedicated AI engine can analyze large quantities of data in split seconds and would be able to identify potential malicious behaviors, vulnerabilities in code or scripts, and correlate events between different systems, sources, and historic data. If properly trained to identify malicious patterns, the AI could, in a very quick timeframe, offer the local Security Teams the chance to analyze and flag the merged events as real positives or false positives. This would finally bring a real proactive risk assessment, giving the chance to correct defects before they're exploited by malicious actors

AI plays a pivotal role in responding to security events. By leveraging insights provided by monitoring tools, AI offers valuable insights on how to mitigate and recover networks or systems from potential threats.

AI (i.e, machine learning and predictive analytics ) can be effective for risk mitigation. In Cybersecurity it can be used to detect and prevent threats by analyzing network traffic and identifying suspicious activities. In the financial sector, AI can be used to analyze market trends, sensitivity implications, optimize investment strategies etc. AI-powered chatbots and virtual assistants can mitigate risks by providing real-time information. These systems are built to handle user queries and provide responses or escalate issues to human operators timely. Despite these benefits. human input and judgment are still crucial in decision-making. AI should be seen as a tool to augment human capabilities and not replace them entirely.

AI is able to plan and orchestrate tasks. What does this mean. AI can analyze if your system is being compromised. If identified, it will inform you of the infected system and propose to shut them down. It can even analyze how the attacker got access including what was modified to achieve the lateral movement. It will correlate this with your open CVE in the system and select the right patch to prevent it in the future. It can even propose a software change if no known patch is available. You see I wrote very often things like "propose". AI system could do this without human interaction but this would create a legal liability of the AI provider. AI is not a legal entity. The decision is done by a human. Even if the CEO decided to use AI

I think main AI advantage in Risk Mitigation is the False Positive analysis and being able to isolate the signal within the noise to make sure actions ate targeted to the real threat.

In mergers and acquisitions, risk mitigation is paramount. Advisory and consulting firms must perform comprehensive due diligence, harnessing both traditional methods and advanced technologies. AI can play a critical role by analyzing large datasets to unearth financial, operational, and compliance risks. It can also predict future market trends and assess the target company's fit within these trends. For thorough due diligence, firms should employ cybersecurity assessments, including autonomous penetration testing to evaluate the digital security posture of the target entity. Additionally, AI can aid in the assessment of cultural and organisational fit, which is often overlooked but crucial for a smooth post-merger integration.

The awesome benefit of AI is that you can simulate how a new CVE in your system can be exploited. It can identify the full kill-chain from the user's device to achieve lateral movement up to the sensitive system for action. As a consequence, you know which attack paths are most likely an can monitor these. Or you decide that the risk is too high and patch the CVE. Keep in mind, while you have always many CVEs in your system they are not equal. Some might be dead ends for attackers, while others can shut down your entire company.

In risk monitoring, AI acts as a vigilant guardian, continuously evaluating the security landscape's pulse. This constant vigilance allows for real-time adjustments, ensuring that risk management strategies remain aligned with the ever-changing threat environment. AI’s ability to forecast potential threats and simulate defensive responses is akin to a grandmaster anticipating moves in chess. This foresight not only fortifies current defenses but also prepares organizations for emerging threats, ensuring enduring resilience.

In risk monitoring, AI greatly enhances our ability to track and adjust our risk management strategies. By utilizing feedback loops and continuous learning, AI measures the effectiveness of our mitigation actions in real-time. This capability allows us to adapt quickly to changing risk environments.

In my experience, natural language processing NLP algorithm of AI can analyse social media, news and other textual data to access public sentiment, which can be useful for predicting and managing reputational risks. Similarly, it can review contracts and legal documents to identify potential risks and ensure compliance with regulations.

In my experience, you need to adjust your communication to your audience. Many security professionals are bad at this. They expect that all are experts. Simplification of messaging sounds easy but can very be hard. AI algorithms can not only analyze an incident but can also directly generate the network of actions, which is nothing more than a graph. Once you have the graph this can be transformed directly into a complete picture. This visualization is extremally powerful to explain the story of the attack including unsuccessful routes but also nearly missed attack diversions. Text is great but nothing beats a compelling graphic because we humans are visual beings.

AI transforms risk communication into an interactive dialogue, bridging the gap between complex technical data and diverse stakeholder groups. By employing AI in translating intricate security data into comprehensible formats, organizations foster a deeper understanding of risks across all levels. This isn’t merely about reporting; it's about creating narratives that resonate with each stakeholder, ensuring that risk awareness permeates the entire organizational fabric. AI's role here is pivotal in building a coherent, transparent, and informed security culture.

I agree, but effective communication is crucial to ensure that the information provided by the AI systems is accessible and actionable for both technical and non technical stakeholders. By incorporating principles such as clear and understandable language, contextual explanations, education and training, ethical considerations etc., into risk communication strategies, organisations can enhance the effectiveness of AI systems in conveying critical information.

Organizations venturing into the world of artificial intelligence (AI) must prioritize AI security and risk management from the outset. To achieve this, they can consider starting with one of the **evolving** risk frameworks tailored to the unique challenges posed by AI. Here are a few notable options: 1. NIST AI Risk Management Framework (AI RMF) 2. Google's Secure AI Framework (SAIF) 3. The EU AI Act 4. MITRE ATLAS? (Adversarial Threat Landscape for Artificial-Intelligence Systems) 5. SAFE AI Framework 6. Secure AI Framework (SAIF) 7. OWASP AI Security and Privacy Guide **Given the rapid pace of developments in this field, continuous research is essential as the information provided may become outdated in the near future.

Embedding ethical principles into AI processes is paramount. Aligning AI with organizational values ensures responsible and transparent risk management. This ethical foundation becomes the guiding compass for AI design, implementation, and evaluation. Utilizing audit trails and transparency mechanisms is crucial for maintaining accountability and trustworthiness. This ensures that the decision-making processes of AI systems are traceable, understandable, and compliant with ethical standards.

The biggest challenge is the shortage of skilled people. We are very reluctant to hire junior employees because a mistake can be very costly to the company. It is completely different with other positions like Data Scientist. They can train their algorithms again and again and again without any negative business impact. A mistake by security analyst will most likely have a direct impact. Therefore, we are reluctant to give them a chance, while someone gave us a chance in the first place. I believe AI supported Junior Cyber Security Analyst to be the future. They have the AI system as their private supervisor over their shoulder. Severe overlooks are much less likely. So let's see AI support as the way forward to give young people a chance

Risk mitigation extends beyond immediate operational risks to include strategic, financial, regulatory, and reputational concerns. For a thorough risk assessment, it's crucial to analyse the target's adherence to industry regulations and the potential for regulatory changes.AI can help scrutinise the target's revenue streams and financial health indicators. Strategically, assessing the alignment of long-term objectives and the integration of technology and talent. Reputational risk also needs scrutiny, as public perception can significantly impact post-acquisition success. AI's data-driven insights can be pivotal in evaluating these diverse areas, providing a multi-dimensional risk profile essential for informed decision-making.

Whilst threat intelligence and detection has been around in various forms in corporates for over 10-15 years, it is still developing and comparatively immature. Professional bodies of knowledge and ways of working are still being developed and codified. This combination of intelligence and data science is used to deal with cybersecurity, fraud, anti-money laundering and insider threat detection - all of which use a combination of AI and non-AI approaches. Whilst the focus differs, the practices, competencies and themes are similar. Collaboration between these functional silos will help improve threat detection, and provide opportunities for practitioners to learn from the experiences of others.

In considering the broader picture, it's important to share that integrating AI into cybersecurity isn't just about technology; it's also about understanding the evolving nature of threats and adapting our mindset accordingly. A key insight from my experience is the importance of balancing AI-driven security with human oversight. While AI excels in processing speed and pattern recognition, human expertise is crucial for interpreting complex scenarios and making nuanced decisions.

Just like with the whole outsourcing issue within DevSecOps, be careful who's developing your business applications as they have access to source code and production data. Just like be careful what AI brain is sniffing your SIEM data and which country controls this data as they know all of your sensitive network traffic and data going through the wire. When it comes to AI, many humans don't understand how it works but they know they need help streamlining cybersecurity issues as they don't have the human power, expertise on staff, or budgeting to increase technology or employees so AI seems like the simple solution? But is it in the long run of AI knowing more about your security risks than your own companies knows?