As artificial intelligence (AI) increasingly permeates the cyber landscape in 2023, Chief Information Security Officers (CISOs) find themselves navigating a complex array of demands. Boards are pressing for comprehensive plans, vendors are touting AI-driven solutions, and internal teams are identifying multiple areas for AI applications—from streamlining Security Operations Center (SOC) triage to enhancing operational technology (OT) readiness. While the potential for AI in cybersecurity is substantial, the accompanying noise can be overwhelming. Establishing clear objectives about what to procure and what to develop in-house is essential for cutting through this noise.
SaaS AI accelerators, which are hosted add-ons that integrate with existing tools, can significantly enhance efficiency. These solutions are designed to minimize time spent on repetitive tasks and increase consistency in outputs. For example, an accelerator that processes telemetry data could draft useful queries, compile incident narratives, and suggest actionable responses—all while maintaining thorough logging. Such tools can yield quick results, often measurable within weeks, without necessitating a complete overhaul of existing systems. This also applies to identity management and email security, where these accelerators can propose safer access policies and conduct targeted phishing training.
On the other hand, Enterprise AI becomes crucial when organizations require reliable outputs and verifiable sources that need to remain within their own network. This is particularly relevant in operational technology, where training for potential attacks should occur in controlled environments. Enterprise AI solutions can facilitate processes that span multiple teams or involve sensitive data, ensuring that all operations adhere to established policies consistently.
Clarity is essential as marketing often blurs the distinction between traditional AI models, which focus on detection and clustering, and generative AI, which creates text, images, or code. In cybersecurity, these two models are frequently paired—detection systems identify signals while generative models assist in drafting reports and making decisions. However, organizations must treat outputs from generative models as preliminary drafts, requiring thorough review and proper documentation, especially for regulatory compliance.
In the fast-paced SOC environment, every second counts. Here, accelerators that can enhance triage speed and improve incident documentation without compromising data security are invaluable. Similar principles apply to identity hygiene and resilience against phishing attacks. Implementing reversible changes and ensuring privacy-conscious telemetry are critical for safe and effective enhancements.
Additionally, Enterprise AI can expedite assessment processes by pre-filling answers based on existing data and presenting control evidence for cleaner reviews. This functionality not only streamlines workflows but also alleviates the burden on business users tasked with completing extensive security and privacy questionnaires. By working within established governance frameworks, AI can enhance both speed and quality while ensuring compliance with privacy and security mandates.
However, it is crucial to approach the hype surrounding AI with a healthy level of skepticism. The fully autonomous SOC remains a distant goal, not an achievable reality in the near term. Human oversight is indispensable; organizations must demand transparency regarding AI-generated suggestions and clearly differentiate between system recommendations and analyst decisions. Relying on unsupervised auto-remediation in live production environments poses significant risks, thus necessitating a cautious approach that prioritizes review and easy rollback capabilities.
Governance should be both rigorous and adaptable. A living inventory of AI systems detailing their functions, data origins, ownership, and logging practices is essential. Coupling this with practical safety measures—including human approval for significant actions and periodic drift tests—can help maintain innovation within acceptable parameters while keeping teams agile.
CISOs can simplify decision-making by posing two fundamental questions: Will the solution integrate seamlessly with existing systems and provide tangible benefits within weeks without disrupting data boundaries? If the answer is affirmative, it qualifies as a SaaS AI accelerator, which should be evaluated based on fit, speed, and auditability. Conversely, if a solution necessitates governance oversight, involves sensitive data, or must operate locally, it should fall under enterprise AI capabilities where organizations maintain control over lifecycle management and audit trails. By contemplating these questions, organizations can better navigate the burgeoning landscape of AI tools and achieve meaningful enhancements in cybersecurity operations.
Richard Watson-Bruhn is a cybersecurity expert at PA Consulting.
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