Gartner’s recently recommended 2026 cybersecurity projects are solid guidelines and savvy. They point to the same inalienable modern truth: security doesn’t begin with another dashboard, another policy PDF, or another tool bought during a lose-it-or-use-it budget scramble. It begins with knowing what exists, what it can access, and how quickly that access can change when the inevitable happens.
This applies whether the work is AI governance, agent discovery, secure development, SOC redesign, IAM attack surface reduction, or business impact analysis. Different projects. Same starting point. Visibility first. Control next. Evidence always.
Gartner’s 2026 cybersecurity trends group around securing new technology, transforming governance, and normalizing AI adoption, including IAM for AI agents, post-quantum planning, agent oversight, regulatory resilience, and AI-driven SOC adoption.
2026 cybersecurity projects
Summing them up, they are listed as follows:
AI cybersecurity projects
- Build a cross-functional governance framework for AI compliance and risk oversight.
- Partner with infrastructure and operations teams to identify AI platforms, agents, and automation dependencies.
- Integrate security controls directly into AI engineering and deployment pipelines.
- Restructure SOC operations to effectively support AI-assisted detection and response.
Resilience projects
- Shrink identity-related attack paths through stronger visibility, monitoring, and remediation practices.
- Modernize business impact analysis to reflect cloud, SaaS, AI, and identity dependencies.
- Create formal defenses against deepfake-driven fraud and impersonation attacks.
- Develop a centralized cryptographic inventory to support crypto-agility and post-quantum transition planning.
AI needs governance that can survive contact with reality
Their first recommended AI project is to develop a hybrid cybersecurity governance model for AI regulation. Sensible, and also overdue.
AI governance doesn’t neatly fit into legal, security, engineering, data, or infrastructure. It touches all of them, then wanders off into procurement. A useful model needs shared ownership: legal for regulatory interpretation, security for control design, engineering for implementation, I&O for operational dependency, and business leadership for risk acceptance.
Gartner warns that AI agents introduce new IAM challenges around identity registration, governance, credential automation, and policy-driven authorization for machine actors. Failure to address this increases the risk of access-related incidents as autonomous agents become more prolific, and agentic AI is the business differentiator and competitive advantage right now.
AI regulation isn’t just about model behavior. It’s about AI access behavior. Which systems can an agent call? Which datasets can it read? Which workflow can it trigger? Which approval can it bypass because someone called it “temporary” and then wandered into the mist?
A practical governance model should define how AI systems are approved, inventoried, monitored, permissioned, reviewed, and retired. Otherwise, “AI governance” is no more than a committee with free pizza. Useful for morale, but far less useful during an incident.
Map the AI system and agent footprint
The second AI cybersecurity project is collaborating with infrastructure and operations (I&O), the organization responsible for running and maintaining core IT environments, to map the AI system and agent footprint.
AI systems aren’t just applications. They’re prompts, plugins, models, APIs, tokens, service accounts, automation chains, cloud roles, SaaS integrations, data stores, and human approval paths. Some are sanctioned. Some are shadow AI. Some are the result of a well-meaning team trying to move faster than the ticket queue. We’ve all met that team. Sometimes we are that team.
Gartner has noted unmanaged agent proliferation as a core risk in 2026, and the World Economic Forum reports that 77% of respondents saw an increase in cyber-enabled fraud and phishing, with phishing, payment fraud, and identity theft among the most common attack types.
Mapping should include human and non-human identities, privileges, API keys, cloud entitlements, data access, third-party connectors, and approval routes. The goal’s not to create a pretty spreadsheet, then abandon it like a gym membership in February. The goal is a living inventory tied to security policy and remediation.
Secure AI development workflows without turning developers into furniture
Enforcing security in AI development workflows shouldn’t mean throwing a 40-page policy at engineers and hoping they’ll stick to it.
Any useful version must be practical. Approved model sources. Secrets management. Access reviews for AI services. Secure CI/CD permissions. Logging for agent actions. Clear rules for where sensitive data can go. Human approval for high-risk automation. Least privilege for every model, agent, and service account involved.
AI development often moves faster than traditional control processes. So the control process has to become easier to use, not easier to ignore.
Identity-led security helps greatly. If an AI workflow needs access, it should request it, justify it, receive only what it needs, keep it only as long as needed, and leave behind evidence. That’s basic hygiene.
Reimagine the SOC around AI, not under it
The SOC doesn’t need to become a call center for machine queries.
AI can help summarize alerts, enrich investigations, find patterns, and reduce repetitive work. Nobody joined security because they yearned to manually triage 400 near-identical alerts before our first coffee. But AI should support judgment, not replace it.
Gartner warns that AI-driven SOC adoption must be handled carefully to avoid weakening analyst capability as automation increases.
The better model is human-led, AI-assisted operations. AI can recommend. Humans decide. High-risk actions require approval. Every automated decision must be explainable enough to be reviewed later. Every identity action must be traceable. Every escalation path must be tested before the crisis, not during the event.
Resilience cybersecurity projects now include deepfake defense
Deepfake defense belongs firmly in the business process, not just tooling.
Deepfakes target trust. Executive voice approvals, payment instructions, HR changes, vendor onboarding, emergency access requests, investor communications, and incident response channels are all vulnerable if the process depends on “that sounded like them.”
The defense is boring, which means it has a chance of working, and it’s not just staff education for our colleagues now desensitized to cyberattacks. Use out-of-band verification, approval workflows, privileged action checks, payment thresholds, identity verification, and clear escalation routes. Make the safe path faster than the risky shortcut.
Cryptoagility starts with finding the cryptography
Post-quantum readiness sounds grand. The first step is less cinematic: find your cryptography.
NIST’s post-quantum migration work focuses on helping organizations move from quantum-vulnerable public-key algorithms to standardized post-quantum algorithms. Microsoft’s 2026 guidance makes the practical point clearly: the hardest part is often finding where cryptography is used across applications, infrastructure, devices, and services.
So build the inventory. Prioritize long-lived sensitive data. Identify dependencies. Plan migration routes. Test crypto-agility before a regulator, board, or future quantum breakthrough asks awkward questions.
IAM attack surface reduction is the connective tissue
Of all Gartner’s recommended cybersecurity projects, reducing the IAM attack surface is the one that supports the others most.
AI agents need identities. Cloud systems need entitlements. SaaS tools need roles. Developers need temporary access. SOC teams need observability. Resilience teams need rapid containment. Auditors need evidence to prove least privilege. Attackers just need one overprivileged account.
The goal is visibility, observability, and remediation across human and machine identities. Find excessive permissions. Remove standing privilege. Time-bound (just-in-time) elevated access. Detect risky paths. Review dormant accounts. Govern service accounts and agent identities. We have to make access changeable without lagging behind the risk it’s meant to control.
Reassess the business impact analysis for the world you actually run
The old business impact analysis (BIA) often assumed neat applications and tidy dependencies. In 2026, this is woefully out of date. In 2027 and beyond, in our current cybersecurity playing field of rapid advances and adoption, with quantum computing and Artificial General Intelligence (AGI) looming on the horizon, there’s no such animal.
The 2026 version needs SaaS, cloud infrastructure, identity providers, AI agents, automation workflows, third-party integrations, cryptographic exposure, and recovery dependencies. Know which identities can stop the business. Ask which agents can change data. Ask which SaaS outage breaks operations. Ask which access paths would make recovery harder.
Gartner’s cybersecurity projects are not eight separate chores. They are one operating principle viewed from eight angles: know what exists, govern what it can access, and change that access fast when risk appears.
That’s the work, and it’s no longer optional. And frankly, much cheaper than explaining to the board that the incident started with a forgotten service account.
If Gartner’s 2026 cybersecurity projects all begin with visibility, access control, and rapid remediation, the next question is: Can we actually see every identity, entitlement, agent, and risky access path operating across our cloud and SaaS estate today?
Start our free trial, and in about 30 minutes, you can map human and non-human identities across AWS, Azure, Google Cloud, and SaaS platforms, uncover excessive permissions, identify dormant or risky accounts, and enforce least-privilege access with time-bound controls and approval workflows. Or, if you prefer, request a demo, and we’ll show you how to reduce the IAM attack surface behind modern AI, cloud, and resilience initiatives before attackers discover it first.
