AI isn’t all about Automation

With the release of ChatGPT and then all the AI tools that quickly followed it, we’ve been bombarded with “We are going to automate blah blah blah!”.

Yeah, right.

There’s automation, and then there’s automation. Let’s review the levels and see how they apply to Governance, Risk, and Compliance (GRC) or Security Operations (SecOps).

Level 1: Introductory Assistance

At the onset of automation, Level 1 introduces foundational support. This mirrors the early days of both GRC and SecOps, where organizations maintained physical binders of policies and regulations and computer security was limited to basic antivirus software scanning for known threats.

In the combined realm of GRC and SecOps, AI at this level acts as a foundational tool, offering insights for enhancement. Just as basic compliance checklists ensure adherence to standards, Level 1 AI evaluates network patterns and proposes basic threat detection alongside regulatory adherence.

Level 2: Enhanced Assistance

Progressing to Level 2, AI begins to assume a more proactive role. This is akin to the transition from manual record-keeping to digital GRC platforms and the evolution to intrusion detection systems (IDS) in SecOps.

In this integrated setting, AI anticipates potential risks, auto-generates compliance and security alerts, and provides more insightful recommendations based on both threat intelligence feeds and regulatory changes.

Level 3: Conditional Automation

Level 3 marks a notable shift towards automation. Reflecting the capabilities of integrated GRC platforms and intrusion prevention systems (IPS) in SecOps, AI offers advanced analytics and threat mitigation but still requires human judgment for decision-making and complex incidents.

In this combined domain, tools that auto-draft audit responses or auto-respond to detected threats exemplify this level, balancing automation with human oversight.

Level 4: Elevated Automation

Delving deeper into automation, Level 4 systems operate with minimal human intervention. This can be likened to predictive GRC platforms that proactively identify potential regulatory changes and advanced threat intelligence platforms in SecOps that autonomously neutralize threats.

A pertinent discussion at this level centers on the expertise required of the compliance officer and the security analyst to interpret and act on AI-generated insights and threat landscapes.

Level 5: Comprehensive Automation

Level 5 epitomizes the zenith of automation. Envision fully autonomous systems in GRC and SecOps, where AI detects and mitigates threats, learns from each incident, updates its algorithms, and ensures continuous protection and compliance without human intervention.

What do pineapples have to do with GRC and SecOps?

Wittgenstein’s statement, “this is a lovely pineapple,” is related to generative AI in the context of language and meaning. AI’s role in the combined domains of GRC and SecOps transcends mere automation. It signifies a shift from reactive measures to proactive governance and threat mitigation based on the complexities of language and the content in which it is applied. Drawing inspiration from the phased evolution of both GRC and SecOps tools and practices is crucial for those navigating this transformative landscape.

Delving into the linguistic philosophy of Ludwig Wittgenstein, we’re reminded of the complexities of language and its contextual understanding. Content is the essence of both GRC and SecOps. Given their vastness and dynamic nature, GRC and SecOps content analysis demands the precision of AI, steering clear of the pitfalls of unchecked automation. Understanding the nuances of language and meaning is crucial for developing natural language processing models. Wittgenstein's emphasis on the contextual nature of language and the multiplicity of interpretations of propositions can be seen as relevant to the challenges faced in training AI models to understand and generate human-like language. Generative AI models need to consider the context and various interpretations of language to produce coherent and meaningful outputs.

Your organization’s AI playbook must underscore the application of AI in the field, focusing on cataloging and content analysis of Authority Documents, Threats, Vulnerabilities, and all other data-driven facts of GRC and SecOps. We can extract, classify, and co-relate information more efficiently by leveraging AI. Techniques such as Natural Language Processing (NLP), rule-based extraction methods, and machine learning algorithms are pivotal in this endeavor. Furthermore, the importance of version control, data exploration, and model training cannot be overstated.

To truly appreciate AI’s value in both GRC and SecOps, one must recognize the challenges it addresses. The benefits of discriminative, generative, and modular models are immense. The potential to harness AI for analyzing GRC language and implementation is not just promising – it’s revolutionary.

At Unified Compliance, we’re committed to staying at the forefront of these advancements, ensuring our solutions are the evolving landscape and consistent with setting the benchmark for excellence in the industry.