Maksim Kabakou - Fotolia

Opinion

Cutting through the noise: SaaS accelerators vs. enterprise AI

The Security Think Tank considers what CISOs and buyers need to know to cut through the noise around AI and figure out which AI cyber use cases are worth a look, and which are just hype.

By
  • Richard Watson-Bruhn, PA Consulting
Published: 13 Jan 2026

AI in cyber is everywhere this year, and if you’re a CISO you’re feeling the push from all sides. Boards want a plan, and vendors are promising AI‑powered outcomes. Your own teams can list a dozen places to apply it from speeding SOC triage, tightening identity hygiene, helping business users complete assessments, strengthening audit evidence, to improving OT readiness. The opportunity is real, but the noise is exhausting. The easiest way to cut through it is to decide, up front, what you’re buying and what you’re building. That means thinking in terms of SaaS AI accelerators and enterprise AI capability.

SaaS AI accelerators are hosted add‑ons that plug into tools you already use. Their job is practical, to  shave time off repetitive work and make outputs more consistent. If something sits on your telemetry, and drafts useful queries, assembles incident narratives, and proposes actions you can approve and roll back, with sensible logs, it will help in days, not months. The same goes for identity and email where accelerators can suggest safer access policies, flag risky sessions, nudge least‑privilege clean‑ups, or run adaptive phishing training. These tend to deliver quick, measurable gains without needing to re‑architect your programme.

Enterprise AI is the right choice when you need trusted outputs and verifiable sources that can be produced entirely inside your network if required. It’s also the right fit for operational technology, where teams should rehearse attacks in a safe testbed and track tangible improvements (faster detection, quicker recovery) rather than just running tabletop discussions. Use enterprise AI when the work spans multiple teams, touches sensitive data, or your policies need it to run the same way every time.

One more clarification is helpful when marketing blurs the terms. AI covers traditional models that detect, score, and cluster and generative AI creates text, images, or code. In security you’ll often pair them and the detection models will surface signals while the generative models will help people explain, draft, and decide. Treat generative outputs as high‑quality drafts to review, log, and tie important statements back to trusted sources, especially for audit or regulatory use.

So which use cases are worth a look?

In the SOC, seconds matter; accelerators that cut triage minutes and improve incident narratives without moving data out of bounds earn their keep. Identity hygiene and phishing resilience are similar.  Reversible changes and privacy‑aware telemetry keep improvements safe and relevant to humans. Enterprise AI can prepopulate assessment answers from known data, surface control evidence, and hand everything to reviewers for clean sign‑off, and replace fatigue with flow. Don’t overlook the value of assessment completion to help business users who have to slog through privacy, security, and vendor questionnaires. AI can suggest answers, highlight gaps, pull relevant policy extracts, and set out the route for approval. When done inside your governance framework, it increases speed and quality while keeping control within your privacy or security office requirements.

When dealing with the hype, you should be realistic. A fully autonomous SOC is still a future headline, not an achievable outcome in 2026. Keep humans in the loop, insist on explainability for suggested actions, and separate “what the system proposed” from “what the analyst did.” Unsupervised auto‑remediation across production environments is similarly risky. It’s important to start narrow, review anything that changes live systems, and make roll‑back easy. Also be wary of perfect detection claims, false positives, false negatives, and drift are facts of life and of instant compliance. If you can’t export and justify evidence for audit, you don’t own it. Ungoverned generative outputs are not a source of truth; they are powerful assistants that still need sources.

The Computer Weekly Security Think Tank on AI hype

Then there’s a need to keep governance light but real. Maintain a living AI inventory that sets out what each system or agent does, where its data comes from, who owns it, and how it’s logged. Pair that with practical safety checks with human approval of impactful actions, apply reversible changes, log prompts and output, and run periodic drift tests. That should mean innovation stays on the right side of expectations without slowing the team.

Finally, make decisions easy with two questions. Will this plug into your existing stack and deliver value in weeks without breaching data boundaries? If the answer is yes, it’s a SaaS AI accelerator that needs to be judged on fit, speed, guardrails, and auditability. Does it need to live inside governance, touch sensitive evidence, or run locally or offline? If yes, it’s an enterprise AI capability where you need to build or extend it so you own the controls, lifecycle, and audit trail. Consider those questions and you’ll be able to secure clear, defensible wins from the wave of AI tools. You’ll have accelerators for tasks where seconds count, capability where governance carries the load, and provide genuine help for the people doing the work including filling in those questionnaires that never seem to end.

Richard Watson-Bruhn is a cyber security expert at PA Consulting.

Read more on Web application security